Biomimetic Artificial General Intelligence (AGI): Taking inspiration from nature for smarter machines
Biomimetic AGI, or Bio-AGI, is a relatively new field of artificial intelligence that seeks to build intelligent machines by drawing inspiration from biological systems.
It’s like looking at how nature has solved complex problems over billions of years and applying those solutions to create the next generation of AGI.
Here’s a breakdown of the key components:
Biomimetic: This refers to imitating or mimicking biological processes and structures. Think of how robots are learning to walk by studying how animals move, or how AI algorithms are being inspired by the intricate neural networks of the brain.
Artificial General Intelligence (AGI): This is the ambition to create machines that can understand and learn like humans, exhibiting genuine intelligence and adaptability across a wide range of tasks.
The aim of Bio-AGI is to combine these two concepts:
- Leverage the efficiency and robustness of biological systems: Biological systems have evolved over eons to be incredibly efficient and adaptable in their environments. Bio-AGI aims to tap into this power by borrowing design principles, learning algorithms, and even materials from nature.
- Develop truly intelligent machines: Current AI systems are often good at specific tasks, but struggle with broader thinking, reasoning, and common sense. Bio-AGI hopes to break through this barrier by learning from the complex and flexible intelligence of living organisms.
Type of Biomimetic Artificial General Intelligence (AGI)
There are multiple ways to categorize Biomimetic AGI (Bio-AGI) based on different aspects. Here are some key types:
By inspiration source:
- Neuro-inspired AGI: Draws inspiration from the structure and function of the nervous system, including neural networks, learning algorithms, and brain-computer interfaces.
- Evolutionary AGI: Mimics the principles of natural selection and evolution to develop robust and adaptable AI systems. This involves genetic algorithms, population-based learning, and self-replication.
- Swarm intelligence AGI: Takes cues from the collective behavior of social insects like ants and bees. This involves decentralized systems, emergent properties, and collaboration among many simple agents.
- Developmental AGI: Focuses on mimicking the way human and animal intelligence develops through interaction with the environment. This involves unsupervised learning, embodied cognition, and sensorimotor integration.
By application:
- Biomimetic robotics: Utilizes biological principles to design and control robots with greater flexibility, agility, and efficiency.
- Biomimetic medical intelligence: Applies biomimetic algorithms to healthcare problems like drug discovery, medical diagnosis, and personalized medicine.
- Biomimetic materials science: Mimics natural materials and structures to create new materials with superior properties for various applications.
- Biomimetic sustainable AI: Develops AI systems that are energy-efficient, resource-conscious, and operate in harmony with the environment.
By technical approach:
- Neuromorphic computing: Designs hardware and software that mimics the architecture and processing of biological neurons.
- Artificial immune systems: Uses principles of the immune system to develop robust and adaptive AI systems for security, anomaly detection, and optimization.
- Swarm intelligence algorithms: Implements algorithms inspired by social insect behavior for optimization, task division, and collective learning.
- Developmental learning algorithms: Employs unsupervised learning and interaction with the environment to develop skills and knowledge autonomously.
Type of Biomimetic Artificial General Intelligence (AGI): Biomimetic robotic
Biomimetic robotics, a fascinating intersection of biology and engineering, seeks to design and build robots inspired by the principles and structures found in nature. It’s about learning from the ingenuity of living organisms and applying those lessons to create machines that are more nimble, adaptable, and efficient.
Here’s a closer look at the key aspects of biomimetic robotics:
Inspiration sources:
- Animals: Their locomotion, agility, and sensory capabilities offer rich inspiration for robot design. Imagine robots inspired by the graceful flight of birds, the stealthy movement of snakes, or the powerful locomotion of insects.
- Plants: Their adaptive mechanisms and efficient energy usage can guide the development of self-repairing robots and sustainable energy solutions.
- Biological materials: Spider silk, bone, and muscle tissues hold clues for creating strong, lightweight, and adaptable materials for robotic construction.
Key areas of focus:
- Locomotion: Creating robots that can move efficiently and gracefully on land, air, or water, mimicking the diverse strategies employed by animals.
- Control and Perception: Equipping robots with sophisticated sensors and intelligent algorithms to navigate their environment, similar to how living organisms perceive and react to their surroundings.
- Manipulation: Developing robots with dexterous manipulators that can perform delicate tasks, inspired by the intricate movements of human hands or animal limbs.
- Self-repair and adaptation: Building robots that can learn and adapt to changing environments, akin to the regenerative abilities and resourcefulness observed in nature.
Benefits of biomimetic robotics:
- Enhanced efficiency and capabilities: Biomimetic robots can potentially navigate rough terrain, manipulate objects with greater precision, and operate in extreme environments better than traditional robots.
- Sustainability and eco-friendliness: Learning from nature’s efficient energy usage and biocompatible materials can lead to the development of sustainable and environmentally friendly robots.
- New applications and solutions: Biomimetic inspiration can unlock new possibilities in areas like search and rescue, healthcare, and exploration, beyond the capabilities of existing robots.
Challenges of biomimetic robotics:
- Complexity of biological systems: Replicating the intricate complexity of natural systems is a significant engineering challenge.
- Computational demands: Biomimetic algorithms and control systems can be computationally intensive, requiring advanced hardware and software solutions.
- Ethical considerations: As robots become more advanced, concerns about autonomy, artificial consciousness, and their potential impact on society need careful consideration.
Examples of biomimetic robots:
- BigDog: A quadruped robot developed by Boston Dynamics, inspired by the agile locomotion of dogs.
- RoboFly: A miniature robotic fly capable of taking off and landing vertically, mimicking the flight mechanics of flies.
- LCM Soft Robot: A soft-bodied robot with artificial muscles, inspired by the flexible movements of octopuses.
Biomimetic robotics is a rapidly evolving field with immense potential to revolutionize various aspects of our lives. By harnessing the ingenuity of nature, we can create robots that are not just machines, but intelligent partners capable of interacting with the world in a more natural and adaptive way.
Biomimetic Artificial General Intelligence (AGI): Biomimetic medical intelligence
Biomimetic medical intelligence, a fascinating field at the intersection of biology and technology, holds immense promise for revolutionizing healthcare. It’s inspired by nature’s ingenious solutions, mimicking biological systems and processes to develop novel diagnostic tools, treatments, and even medical robots.
Imagine:
- Tiny, snake-like robots slithering through blood vessels, delivering targeted medication directly to diseased tissues. These robots, inspired by the slithering movements of real snakes, could navigate complex vascular networks with ease, minimizing invasive procedures.
- Regenerative medicine scaffolds mimicking the intricate architecture of bone, cartilage, or even organs. These scaffolds, printed with biocompatible materials and designed to mimic natural tissues, could guide and accelerate tissue growth, potentially even leading to organ regeneration in the future.
- AI algorithms trained on vast datasets of medical images and biological data. These algorithms, inspired by the human brain’s pattern recognition abilities, could assist doctors in diagnosing diseases with greater accuracy and speed, even predicting individual patient responses to treatment.
These are just a few glimpses into the exciting world of biomimetic medical intelligence. Its potential applications are vast, and the field is rapidly evolving with new discoveries and advancements.
Here are some key areas where biomimetic intelligence is making significant strides:
- Drug discovery and development: By understanding how natural molecules interact with biological systems, researchers can design more effective and targeted drugs with fewer side effects.
- Surgical robots: Biomimetic robots with enhanced dexterity and precision could assist surgeons in minimally invasive procedures, leading to faster recovery times and improved patient outcomes.
- Prosthetics and implants: Biomimetic limbs and implants that seamlessly integrate with the human body could restore function and improve quality of life for individuals with disabilities.
- Personalized medicine: By analyzing individual patient data using biomimetic algorithms, doctors can tailor treatments and therapies to each patient’s unique needs and genetic makeup.
The ethical considerations of this field are crucial and require careful attention. Issues like privacy, safety, and potential misuse of biomimetic technologies must be addressed proactively to ensure their responsible development and application.
Biomimetic medical intelligence represents a powerful paradigm shift in healthcare. By harnessing the wisdom of nature, we can unlock innovative solutions to some of our most pressing medical challenges and pave the way for a healthier future for all.
Biomimetic Artificial General Intelligence (AGI): Biomimetic materials science
Biomimetic materials science is a fascinating field that seeks to unravel the secrets of nature’s ingenious materials and replicate them to create next-generation materials with groundbreaking properties. It’s like looking to nature as a living laboratory, brimming with solutions honed over millions of years of evolution.
Imagine materials that:
-
Heal themselves like a gecko’s tail: Imagine construction materials that repair cracks automatically, extending their lifespan and reducing maintenance costs. Bio-inspired polymers with self-healing abilities are being developed, mimicking the intricate repair mechanisms found in nature.
-
Repel water like a lotus leaf: Think self-cleaning surfaces that stay pristine without harsh chemicals. Superhydrophobic materials inspired by the waxy nano-structures on lotus leaves repel water and dirt, offering potential for stain-resistant clothing, anti-icing coatings, and even microfluidic devices.
-
Bond with the body like a spider’s silk: Envision medical implants that seamlessly integrate with tissue, minimizing rejection and infection risks. Biocompatible materials inspired by spider silk and mussel adhesive proteins are being engineered, promoting tissue growth and improving the success of implants.
-
Strong yet light like a spider’s web: Picture buildings and airplanes ساخته شده from materials that are incredibly strong yet ultralightweight. Biomimetic composite materials inspired by spider webs and seashells could revolutionize construction and transportation, reducing fuel consumption and emissions.
These are just a few glimpses into the remarkable world of biomimetic materials science. Its potential applications are vast and span across various sectors, including:
- Robotics: Biomimetic materials can create robots with enhanced dexterity, adaptability, and resilience, enabling them to operate in diverse environments.
- Medicine: Biocompatible materials can lead to breakthroughs in tissue engineering, prosthetics, and drug delivery, improving patient outcomes and quality of life.
- Energy: Bio-inspired materials can pave the way for efficient solar cells, wind turbines, and energy storage solutions, contributing to a sustainable future.
Challenges and Considerations:
While the potential is immense, biomimetic materials science is still in its early stages. Challenges exist in:
- Accurately replicating complex natural structures and functionalities.
- Scaling up production processes for cost-effectiveness and widespread adoption.
- Addressing potential ethical concerns around biomimicry and its implications for the natural world.
Despite these challenges, the rapid advancements in materials science, biotechnology, and artificial intelligence are continuously pushing the boundaries of what’s possible. Researchers are constantly developing new techniques and tools to understand and harness nature’s wisdom, opening up a future filled with revolutionary materials that could solve some of humanity’s most pressing challenges.
Biomimetic materials science is a testament to the power of learning from nature. By emulating its ingenious solutions, we can create a future where materials are not just functional, but also sustainable, adaptable, and even life-like. This field holds the potential to transform the way we live, work, and interact with the world around us, making it a truly exciting area to watch in the years to come.
Biomimetic Artificial General Intelligence (AGI): Biomimetic sustainable AI
The convergence of biomimicry and artificial intelligence holds immense potential for creating sustainable AI solutions that benefit both society and the environment. This exciting field seeks to draw inspiration from nature’s resourcefulness and efficiency to tackle the challenges of resource consumption, energy demand, and environmental impact associated with traditional AI approaches.
Here are some exciting possibilities at the intersection of biomimetic and sustainable AI:
-
Bio-inspired algorithms: Instead of relying on massive data centers and energy-hungry computations, AI can learn from natural optimization algorithms found in ecosystems. For example, swarm intelligence, inspired by ant colonies or bird flocks, can solve complex problems with minimal energy expenditure.
-
Self-powered AI systems: Imagine robots that harvest energy from their surroundings, like a butterfly’s wing extracting energy from sunlight. Biomimetic energy harvesting technologies can power AI systems with renewable sources, eliminating dependence on fossil fuels and reducing their carbon footprint.
-
Biodegradable AI components: Traditional AI relies on hardware with significant environmental impacts. Biomimetic materials science can create biodegradable and recyclable components for AI systems, minimizing e-waste and promoting a circular economy.
-
Nature-inspired data analysis: By mimicking the adaptive learning techniques of natural systems, AI can analyze data more efficiently and with less computational power. This can lead to the development of smaller, more sustainable AI devices that require less resources.
-
Environmental monitoring and protection: Biomimetic AI systems can be deployed for real-time environmental monitoring, assisting in tasks like wildlife conservation, pollution detection, and natural disaster prediction. Their energy-efficient nature makes them ideal for long-term deployments in remote areas.
But implementing biomimetic sustainable AI comes with its own set of challenges:
- Understanding complex natural systems: Accurately translating natural processes into algorithms and hardware remains a challenge.
- Scaling up bio-inspired technologies: The commercial viability and scalability of some biomimetic solutions need further development.
- Ethical considerations: The potential impact of AI on ecosystems and the role of human intervention must be carefully considered.
Despite these challenges, the potential rewards of biomimetic sustainable AI are undeniable. By embracing nature’s wisdom and combining it with the power of AI, we can create a future where technology and the environment coexist in harmony. This emerging field holds the key to building a more sustainable and prosperous future for all.
Technology in Biomimetic Artificial General Intelligence (AGI)
Biomimetic AGI, a fascinating subfield of artificial intelligence, seeks to build AGI systems by drawing inspiration from biological intelligence. This involves leveraging various technologies to understand and replicate, albeit computationally, the functionalities and capabilities of the brain and nervous system.
Here are some key technologies powering biomimetic AGI:
1. Neuromorphic computing: This technology aims to build hardware and software architectures that mimic the structure and function of the brain. Instead of traditional silicon chips, neuromorphic chips use analog circuits and interconnected processing units for parallel processing, similar to how biological neurons operate. This potentially leads to more efficient and energy-saving computation, crucial for complex AGI tasks.
2. Artificial neural networks (ANNs): Inspired by the interconnected network of neurons in the brain, ANNs are mathematical models that learn and improve through training on large datasets. Biomimetic approaches involve incorporating biological principles like Hebbian learning (strengthening connections used frequently) and synaptic pruning (eliminating unused connections) into ANNs, enhancing their learning capabilities and adaptability.
3. Evolutionary algorithms: Inspired by natural selection, these algorithms simulate the process of evolution through iterative generations. By evaluating and breeding populations of potential solutions, biomimetic AGI systems can potentially discover novel solutions to complex problems, similar to how biological organisms adapt to changing environments.
4. Sensorimotor control systems: Taking inspiration from biological sensory systems and motor control pathways, these systems enable robots and other AI agents to interact with their environment and respond to stimuli in real-time. Biomimetic sensors, such as artificial olfactory and tactile sensors, could provide richer environmental data for AGI systems, improving their perception and action capabilities.
5. Embodied cognition: This concept emphasizes the importance of an agent’s physical embodiment in shaping its cognition and behavior. Biomimetic robots with sophisticated sensory-motor systems and embodied learning algorithms could interact with the world in a more natural and dynamic way, improving their ability to learn and adapt in complex environments.
Challenges and Future Directions:
Biomimetic AGI faces significant challenges. Accurately mimicking the intricate and complex processes of the brain remains a major hurdle. Additionally, scaling up these technologies for practical applications requires considerable advancements in hardware and software capabilities.
However, the ongoing research and development in fields like neuroscience, materials science, and artificial intelligence suggest a bright future for biomimetic AGI. By continuously refining and integrating these technologies, we can potentially develop AGI systems with a level of adaptability, efficiency, and environmental awareness that surpasses current AI systems.
The potential applications of biomimetic AGI are vast, ranging from robotics and healthcare to sustainable development and space exploration. By harnessing the wisdom of nature and the power of artificial intelligence, we can create a future where technology seamlessly integrates with and enhances our lives in ways we can only begin to imagine.
Conclusion for Biomimetic Artificial General Intelligence (AGI)
Biomimetic AGI, at its core, represents a fascinating paradigm shift in our approach to artificial intelligence.
It is a bold endeavor to not simply surpass biological intelligence, but to learn from its core principles and apply them in the creation of truly intelligent machines. The journey is rife with challenges, demanding breakthroughs in hardware, software, and our understanding of the brain itself.
Yet, the potential rewards are beyond measure. Biomimetic AGI promises systems that are adaptive, efficient, and profoundly connected to the world around them. Imagine robots that learn and repair like living organisms, AI assistants that collaborate with humans in perfect harmony, and technologies that solve our most pressing challenges with the ingenuity of nature itself.
While the path ahead is long and winding, the convergence of biomimicry, neuroscience, and artificial intelligence has ignited a spark of possibility. Biomimetic AGI is not just a technological pursuit; it is a journey of rediscovering the wisdom of nature and harnessing its power to create a future where technology and humanity flourish together.
Therefore, it is not solely about a “conclusion” for Biomimetic AGI, but rather about embracing the continuous, evolving nature of this field. It is a story that will unfold alongside advancements in science and technology, constantly pushing the boundaries of what is possible. As we delve deeper into the secrets of the biological world, the possibilities for Biomimetic AGI to shape our future become both more tangible and more awe-inspiring.
Perhaps the most fitting conclusion, then, is this: Biomimetic AGI is not an end, but a bridge to a future where intelligence, both natural and artificial, can collaborate to create a world that is more sustainable, more adaptable, and more truly alive.
https://www.exaputra.com/2024/01/biomimetic-artificial-general.html
Renewable Energy
Motordoc Diagnoses Drivetrains From the Ground
Weather Guard Lightning Tech

Motordoc Diagnoses Drivetrains From the Ground
Howard Penrose of MotorDoc joins to discuss the circulating currents killing main bearings and drivetrain checks without a climb. Reach out at info@motordoc.com or on LinkedIn.
The Uptime Wind Energy Podcast is brought to you by Weather Guard Lightning Tech, creators of the StrikeTape Ultra LPS retrofit. Subscribe to Uptime’s Substack newsletter. And check out Rosemary’s “Engineering with Rosie” Youtube channel. Have a question we can answer on the show? Email us!
Welcome to Uptime Spotlight, shining light on wind energy’s brightest innovators. This is the progress powering tomorrow.
Allen Hall: Howard, welcome back to the program.
Howard Penrose: Hey, thanks for having me.
Allen Hall: It’s about time everybody realizes what motorDoc can do. There’s so much technology, and I’ve been watching- Yeah … your Chaos and Caffeine podcast on Saturday morning, which are full of really, really good information about the motorDoc as a company, all the things you’re doing out in the field, and how you’re solving real-world problems, not imaginary ones- Yeah
real-world problems. Oh, yeah. Yeah, and
Howard Penrose: whatever annoys me that week. Exactly. And, and whatever great coffee I’m trying out. Yes. Except for a few. We’ve had the ReliaSquatch down our- Yes … um, a couple of times. Uh, yeah, no, I, I enjoy it, and we gotta get you on there sometime. I don’t do- I, it- … a lot of interviews other than an AI character we put in.
Allen Hall: It’s a very interesting show because you’re [00:01:00] getting a little bit of comedy and humor and s- Yeah … and a, and a coffee review, which is very helpful because I’ve tried some of the coffees that you have reviewed, that you’ve given the thumbs up to. But if you’re operating wind turbines and you’re trying to understand what’s happening on the drivetrain side, on the generator, everything out to the blades even, main bearings, gearboxes- Yeah
all those rotating heavy, expensive parts, there’s a lot of ways to diagnose them-
Howard Penrose: Yes …
Allen Hall: that are sort of like we can look at a gear, we can look at a joint, we can look at roller bearings, whatever, but motorDoc has a way to quickly diagnose all of that chain in about- Yeah … 15 seconds.
Howard Penrose: Well, a little longer than 15 sec- more like a minute.
A minute, okay. It feels like paint drying. But- Uh, in any case, yeah. Uh, uh, and, and what’s kind of funny is, um, back in the ’90s, uh, EPRI actually accidentally steered the technology away from its [00:02:00] core purpose, which was in 1985, um, NAVSEA, the US Navy, had done research on using current signature analysis for looking at pumps, fans, and compressors, the bearings, the belts, the components, all the rotating components using the motor as the sensor.
Not too much different than we are now. I mean, mind you, we got better resolution now, we’ve got, uh, more powerful– I mean, I look at my data from the ’90s, and now it’s completely different. Um, and then Oak Ridge National Lab, same thing, bearings and gears in motor-operated valves. So in 2003, we were the first ones to apply electrical and current signature analysis to some wind turbines in the Mojave Desert.
Wow. Yeah. So, um, nobody had tried it before. Everybody said it couldn’t be done. And, uh, that was a bad thing to say to me because- … it meant I was gonna get it [00:03:00] done. Right. At that time, um, we were looking at bearing issues and some blatant conditions with the, um, with the, uh, generator using a technology called Altest, ’cause I was with Altest at the time.
And, uh, I had taken an EMPath software and blended it with a, a power analyzer, and they still have that tool to this day. I was using that technology all the way through 2015. 2016, I should say. And then- And then switched over to the pure EMPath, which was more of an engineering tool. And then more recently, in 2022, uh, made the decision to ha- to take all the work we’d done on over 6,000 turbines, uh, looking at how we were looking at the data and what we were doing on the industrial side, and took a, uh, created a current signature analyzer that would do one phase of current to analyze the entire powertrain.
Allen Hall: So when you tell [00:04:00] operators you can do this magic, I think a lotta times they gotta go, “
Howard Penrose: What?” Oh, yeah, yeah. They don’t understand it because they’re used to vibration- Right … which is a point analysis system. Right.
Allen Hall: Vibration at this- Yeah … particular location. Yeah. One spot- Even if it’s- … or a couple
Howard Penrose: spots
triax, they’re reading through material, up through a transducer. Hopefully, they put it above the bearing and not in the middle of the machine like everybody is now, because everybody’s trying to sell a sensor. Right. True. They’re not selling a- they’re not selling accuracy. They’re just selling sensors.
Right. So, um- Yeah … you know, uh, I, I’ll, I’ll even talk about one of the companies here. We’ve got Onyx here, and they do it right. I mean, they’ve been doing it right pretty well because we’ve been doing some of the same towers they’re on, and we can match the data they’re getting. Oh, good. Right? Yeah. Uh, so but they get it in multiple spots, and there’s areas they can’t quite reach, so we’ll detect those areas as well.
So it’s a good melding of two technologies.
Allen Hall: Oh, sure. Sure,
Howard Penrose: sure. You know what I mean? Yeah, yeah, yeah. So when you have electrical signature and you have vibration, but in [00:05:00] cases if you don’t have vibration, we’re a direct replacement.
Allen Hall: Because the generator- I
Howard Penrose: dare say that.
Allen Hall: Yeah. Whichever–
Howard Penrose: I dare say that, um, with- Well, the
Allen Hall: generator is acting as the sensor.
Howard Penrose: The air gap. The air gap in the generator s- specifically, yes. Yeah. Generator, motor, transformer. Right.
Allen Hall: Yeah. So any of those- Mm-hmm … you can clamp onto, look at the current that’s on there. Everything that’s happening on the drivetrain, in the gearbox, out on the rotor- Yep … main bearings, all of that creates vibration.
Creates a torque. T- a, a torque. Yeah. Yes, more exactly a torque. Yeah. And that’s seen in the generator, in the current coming out of the generator. Yes. So those signals, although minute, are still there. Yes. So if you clamp onto that current coming out of the generator, you’ll see the typical AC sine wave sitting there.
But on top of that- Is all the information about how that drivetrain is doing
Howard Penrose: Absolutely, and everything else. Anything electrical comes through [00:06:00] that. So what you do is just like vibration, you do a spectral analysis. So every component has a frequency associated with it, just like vibration. It’s, as a matter of fact, I, I keep having to try to explain to people electrical and current signature analysis is no different than vibration analysis.
It’s the same concept. We use the same tools. The signature looks just a little different. It’s a little noisier, um, but you need that noise in order to see everything. But we have a time waveform, and instead of, um, inches per second or millimeters per second, whatever, you know, uh, velocity, acceleration, and displacement, uh, what we end up with is decibels is the optimal method.
You can look at straight voltage signatures at those points or, or current signatures, but the values are so small that you have to look at it from a logarithmic standpoint. Right. There are some benefits to it versus vibration, and there’s some things that aren’t as good as vibration. [00:07:00] So, you know, we, we do…
You have to… Any technology is gonna have their strengths and weaknesses. Sure. So we will see everything all at once. Load doesn’t matter. Right. Speed doesn’t matter. It’s… Only reason speed matters is the location of the frequencies. Uh, so the higher the resolution, meaning the longer you take data, the less chance you have on a lightly lo- loaded machine of blending the peaks together.
Right. Um, on the flip side, if I have two bearings turning at the exact same speed, I couldn’t tell you which one it is. Because they’re the same. Right.
Allen Hall: And the mechanical features of that bearing is w- what creates the signal that you’re measuring. Exactly. So if a bearing has five rollers versus 10, just imaginary thing.
Yeah, yeah. Five rollers versus 10 has a different electrical signature, so you can determine, like, that bearing, that 10 roller bearing- Yes … has the problem, the five is fine. Yes. Yeah. That’s the magic, and I think people don’t translate the mechanical world into the electrical world. That that’s what’s [00:08:00]happening.
They,
Howard Penrose: they don’t because, because what’s happening is they named it wrong.
Allen Hall: Yes.
Howard Penrose: A majority of our users are mechanical folks. Sure. Our vibration analysts and stuff like, ’cause they know how to look at the signatures. Right. Everybody tries to force it on their electrical people, and electrical people go, “We don’t know what this is.”
Yeah. And it’s, it’s, it’s a matter of that training and, and, you know, in the electrical world, you’re not taught to look at that. Right. Yeah. It doesn’t matter. Mechanical world, you’re taught to look at that. So our intern, we were trying to bring in electrical engineering interns and found out that just wasn’t working.
So last year, I brought in my first, uh, intern that’s, you know, he’s been with us now since I brought him in. Okay. Uh, and, uh, Amar, and, uh, you know, he’s helped us develop our vi- uh, vibration software to go along with it. Guess what? It’s the same thing. It’s the exact same sy- system Um, but we just take in a vibration signal instead.
But he picked up on it immediately as a [00:09:00] third-year college student. I can take somebody with a decade as an electrical engineer with a PhD and they can’t figure it out.
Allen Hall: Well, because you’re, you’re taking real- Because it’s different. Yeah. It’s r- well, it’s real-world components-
Howard Penrose: Yeah …
Allen Hall: creating electrical signals.
That’s hard- Well, you have- … to process for a lot of people. Yeah,
Howard Penrose: yeah. It’s
Allen Hall: just not
Howard Penrose: something that we do every day. But that’s… If they, i- if we sa- i- i- if you’re looking at vibration and you start looking at the sensor, it gets complicated too, ’cause guess what? It’s an electrical signal. Right. It’s, it is technically electrical signature now.
It’s converting a
Allen Hall: mechanical signal- Right … into an electrical signal, which is what’s happening in the generator anyway. Yeah.
Howard Penrose: Whether it’s a piezoelectric cell that’s generating a small signal- Yeah … on top of a small waveform that you then take out, you demodulate, uh, or it’s, uh… So you take that carrier frequency out, or it’s a MEMS sensor, which is the same thing.
You know, the, it just sees some slower s- It, it does more of a digital output. So you, you, you know, you have those, or you [00:10:00] have this, which just basically uses a component of the machine to, to, as its own sensor. There is one other difference between them, too, and, uh, I find this very useful when I’m going out troubleshooting something that other people can’t figure out, uh, ’cause we use all the technologies.
So in this case, it would be, uh, the structural movement. Okay? So, so say I have a generator and there’s something wrong with the structure, and the whole machine is vibrating. So y- well, if I put a transducer on it, they might think that’s vibration or something else. We don’t see it. Right. We only see directly exactly what’s happening with the machine.
Sure. So a lot of times when we go in to troubleshoot something that people have done vibration on and everything else, it’s been pro- a, a problem for them for years. We walk in, and all of a sudden we’re identifying whether it’s the machine or it’s something else right off the bat. Then we can take a look at the vibration data and [00:11:00] say, “Okay, it wasn’t the bearing or the bearing, um, structure.
It was, you know, the mounting.” Right. It wasn’t
Allen Hall: fastened
Howard Penrose: down properly. Yeah,
Allen Hall: yeah. Right.
Howard Penrose: Go tighten that bolt. Right, exactly.
Allen Hall: Well, I mean, that’s the cheap answer. Yeah. I’d rather tighten a bolt than rip apart a motor or a generator- And, and- … every day …
Howard Penrose: and that’s the whole point. Now, there are other strengths that go with it.
So for instance, on the powertrain of a wind turbine, I can tell you if you’ve lubricated the bearings correctly. Wow. Because part of what we do is we do take those electrical signatures, and we convert those over to watts. Watts is an energy conversion. Sure. So you see that as heat or some type of loss.
So whatever, whatever’s being lost there is not being sent to the customer. To the outside. Right. Making money. So, um, if I’m taking a look at, say, a main bearing, I might see watts or kilowatts of losses. So you’re gonna have some ’cause you have friction, right? But when we see it increase on, say, a roller, [00:12:00] or the rollers, or, or the cage, that’s usually an indicator that I have a lubrication issue.
Or if we only see it on the outer race, that means that they didn’t clear out all the old grease when they were lubricating it, ’cause the rollers then have to ride across it- Right … ’cause it dries up.
Allen Hall: Sure.
Howard Penrose: Uh, and will carry contaminants. So if you see that, you go up, clean it up, you’ll extend the life of the bearing.
Absolutely you will. Without having to do a lot of work. So, uh, we, we look at our technology as more so early in the, in the stage of a condition. I don’t wanna call it failure, ’cause it’s not a failure. It’s something that’s mitigable. And I made that word up. You can mitigate it. Meaning you can go up and correct it and extend the life of that component.
Sure. Uh, in gearboxes we’ll see problems with, um… Well, the, the one we’re talking about here a fair amount is all the circulating currents going on uptower. We did that research. The current signature analyzer we have is a direct result of doing wind turbine [00:13:00] research just on circulating currents uptower, ’cause we conferred everything over to, to sound at 48 kilohertz.
And so that gives me a 24-kilohertz signal. That high-frequency stuff, which we’re researching in CGRE, and IEEE, and IEC, is called supra harmonics, which I– we talked about that before. Yes, we have. Yeah. And, uh, so when you start seeing that in the, in, in the current that’s circulating uptower because the ground that goes from the top of the tower down is for- DC
lightning protection. And lightning protection, yeah. It’s not meant for, um- Not for
Allen Hall: high frequency- Yeah …
Howard Penrose: currents. Yeah. Uh, we, when we measured it, when we mapped out dozens of towers of all different manufacturers, we found that the impedance about halfway down the tower is where it ends. Sure. The, the resistance.
And then the increased, uh, the high-frequency noise turns any of your shaft brushes into resistors. And at about 15 kilohertz, no current is [00:14:00]passing through them. It’s all passing the bearing, which becomes more conductive the higher the frequency. So with 60% of main bearings failing due to electrical currents, it’s actually currents that are circulating uptower.
It’s not static. There is some static up there, but it’s not static. It’s coming from the controls, the, the generator, and everything else. Inverters,
Allen Hall: converters.
Howard Penrose: And we’ve seen up to 150 amps passing through a, through a bearing.
Allen Hall: So I– We run across a lot of operators who have been replacing main bearings, and they don’t know the reason why.
Yeah. And I always say, “Well, call Howard at MotorDoc because I would almost bet you you have the f- high frequency running around uptower in the nacelle- And the next main bearing you put in there is gonna go the same way as the- Yeah … first one you put in there. Until you cut off that circulating current and then the cell, you’re just gonna continue with the problem.
Then you haven’t eliminated the problem, you’re just fixing the result of that problem. Yes. But it takes- Yeah, you’re, you’re- How, [00:15:00] how, well, how long- You’re replacing
Howard Penrose: a fuse.
Allen Hall: Right, you’re replacing a fuse. Yeah. How long does it take you to s- to determine- An expensive fuse. Yeah. Yeah. Oh, yeah, ’cause you’re taking the rotor down.
Yeah. Well, how, how fast can you determine if you have harmonics uptower that are gonna be causing you problems? 120 seconds.
Howard Penrose: Okay.
Allen Hall: So that’s the thing. I think a lot of- I mean,
Howard Penrose: that’s of the actual data collection time. So you clamp on uptower, uh, and then you can… Well, the way we have it set up now, you just tell it you wanna collect data every five s- uh, five minutes, and then you go downtower, let it collect its data, go back up, grab it.
Um, it’s like…
It’s huge. It’s this size. So, um, and then you connect- It plugs into a laptop. Yeah. Plug it into a laptop or any type of tablet. Um, it, it’s Windows now. I’m trying to get away from Windows. We’re gonna have Linux systems, uh, as well. Uh, and then you use that to, um, just collect that data, and then you press another button.
Now it pops up, and it tells you if you’re in danger or not, [00:16:00] the amount of current passing through the bearing, and the frequencies all the way out.
Allen Hall: So the ideal is you’re gonna have this kit with you in the truck. Yeah. And as you see these problems pop up, you’re gonna clamp on uptower. Yep. You’re gonna measure these circulating currents, and you’re gonna know immediately if you have another mechanical issue, a, a lubrication issue- Oh, yeah.
It’ll look at- … some kind of alignment issue, or- You’ll get all
Howard Penrose: of this information at once. So you- Right … if you go on the power side. So certain turbines, like anything that has the transformer downtower, you don’t have to climb. Right. GE. I mean, I don’t climb. So, uh, uh, you know, th- and that was part of the, the concept behind when we started down this path because I’ve been in the wind industry since 1997.
So one of the things I always saw was, and, and we talked about even, you know, here when it was called AWEA, and we were talking always on the health and safety side about wearing out the technicians. Um, so we discovered that, you know, what was it? Almost 60% of the [00:17:00] turbines you didn’t have to climb. Right.
Oh, yeah. And even the ones you do, you go up, you set it up, and it’ll tell you where you need to focus. The other thing in the powertrain, let alone the generator, when we do a sweep of a site– Now, if we do a straight electrical signature analysis, I’d term that one as a technician’s tool. Sure. That’s more of an engineer’s tool.
Uh, a lot more data, a lot harder to set up. But even though I’m saying harder to set up, it’s still pretty easy. It’s still minutes. Right. Yeah. Most technicians will collect data with, like, a couple hours worth of training. Yeah. You g- You basically gather that data, and if you’re getting a site, so we’ll go out– I love going out in the field.
So we’ll go out in the field, especially if it’s a tower we don’t have to climb I’ll knock out, uh, well, let’s just say I’ll, I’ll, I’ll name one. Say a GE 1.6. I’ll knock out one of those every eight to 11 minutes, depending on how you get to the tower.
Allen Hall: So that’s a full diagnosis of drivetrain- Yeah … plus anything odd happening- Yep
with circulating currents and all that [00:18:00] can- Oh, no, no. Circulating- Or just- … current, that’s a- That’s a separate thing at tower … separate study that- Okay … you have to do that uptower. But anything, anything drivetrain-wise, you can be in and out- Yeah … in a couple of minutes. Yep. Okay. So there’s a lot of operators that have end-of-warranties coming up, right?
Yes. There’s been a lot of developments, so they’re kind of running into the end-of-warranty, and they don’t know the health status of their drivetrain. Same thing for a lot of operators that are in- Yep … full service agreements, and they’re questioning whether they’re getting their money’s worth or not.
Yes. I always say, “Call Howard at Motordoc. You guys can have a whole site survey done maybe in a couple of days, and you will know all the problems that are on site for the lowest price ever”. Yeah. It’s crazy how fast you can do it and how accurate it is. I talk to operators that use your system, so I hear you.
Yeah. Your podcast, listen to your podcast, I’m calling your customers to find out what they say, and they love it. Oh, yeah. They can’t believe how accurate it is. Yeah. Well, the thing about that is we as an industry need to make sure that our turbines are operating at [00:19:00] maximum efficiency. Yep. And if a simple tool like the Motordoc EMPath system exists, we need to get customers, operators in line to start doing it worldwide.
Australia- Oh … Europe-
Howard Penrose: Yeah. We- … Canada. Australia, we’re trying to get into, but right now we even have OEMs using it through North- That’s good … and South America, Asia. Good. Uh, Middle East, um, and, uh, and some of Europe. Good. So it’s, it’s, it’s really taking off. Uh, I’d say probably our biggest market right now is Brazil.
Sure. They’re going crazy. Well, the, the turbines are- They’re having a lot of problems. Yeah.
Allen Hall: Right. And the, well, those turbines have a h- high usage, right? So because- Oh, yeah … the winds are so good, they’re operating at, like, capacity factor is above 50%. Yes. It’s insane. Yeah. So there’s a lot of wear and tear.
There’s no downtime for those turbines.
Howard Penrose: Yeah. Well, and, and people think it’s all the starting and stopping. It’s not. No. It’s a grid-related issue. So we have- Sure … we have a low frequency. And you know some of the stuff I volun- I, I’m, I’ve been volunteered for- [00:20:00] Yeah … uh, including the CIGRE thing. Um, so I get to sit in the grid code committees for IEEE and put my, and our input into that, uh, and kind of watch the back of the IBR industry, right?
Mm-hmm. ‘Cause there’s a definitely bias against our industry. Um, and I also, uh, get to hear what’s going on in the grid side of things from CIGRE worldwide, and it’s all very similar, and it has to do with low-frequency oscillating currents- Yes … called subsynchronous currents- Yes … which are low enough not to damage large synchronous machines.
And they thought, and there’s books written on this, by the way, multiple books written on wind turbine impact- Uh, and they’re seeing now, um… Well, we detected it first, along with Timken. Hank, uh, and, and I went out to a site, and we detected for the first time, because of how they wanna do the testing and where the site was located, we saw the oscillating torque [00:21:00] in the air gap, ’cause that’s one of the things the technology does.
It actually measures the torque, air gap torque. Sure. So we were watching the oscillating torque as a tower started up. And so we did, we went through the rest of that site looking at the same stuff in the same way. It increased our time and data collection, and time on site. But then we started looking for it at other sites, and going to pass data because I don’t have to go back and retake data.
Right. And we’re like, “Oh my God. It’s everywhere.” 16 hertz, 21 hertz, and 50 hertz. And we found a paper that specifically identified that as the sub synchronous frequencies for 60 hertz. So we know what they are also for 50 hertz. Once we identified that and we saw how much the torsi- torque was oscillating, we worked with Shermco, who got us some information on Y-rings that were failing.
Yeah. And they were all failing… When the metallurgy was done, they were all failing from fatigue. And you’re like, fatigue how? What’s fatiguing these connections? [00:22:00] Well, the fatigue is that air gap torque- Exactly … because you’re basically causing the, the, everything to oscillate a little bit, and that causes the windings to move slightly.
It’s a living,
Allen Hall: breathing machine-
Howard Penrose: Exactly … this generator
Allen Hall: is.
Howard Penrose: Yeah.
Allen Hall: It’s not
Howard Penrose: static. It’s definitely not sta- no electric machine is static. No. Even a transformer’s not static. Right.
Allen Hall: So- There’s a little
Howard Penrose: bit of wiggle going on there all the time All the time. And it’s minute, so it takes a long time. Right. And what, uh, uh, everybody…
Well, first people thought it was a particular manufacturer, which it wasn’t. Turned out every defig’s failing the same way. Sure. You’re fatiguing it. Yeah. Every bearing is failing the same way, even in the gearbox, main bearings, and everything else. Right. All of these conditions are happening across all the OEMs, but they’re not allowed to talk.
Well, this is, this is the thing that
Allen Hall: I like watching your podcast.
Howard Penrose: Yeah.
Allen Hall: The Chaos and Caffeine. It comes out Saturday mornings. It’s on YouTube. If you haven’t- Yeah … clicked into it, you should click into it
Howard Penrose: because a lot of these issues are discussed there. It’s definitely, um… [00:23:00] Let’s just say I’ll speak Navy quite a bit.
Allen Hall: It’s a great podcast, and I think what you’re doing with the EMPath system- Yes … at motor dock is really a game changer. Yeah. I’m talking to everybody, all the operators I know. I keep telling them to call you and to try the system out because it’s so inexpensive and it does the work quickly and efficiently, and it’s been proven.
There’s no messing- Oh, yeah … around when you’re talking to MotorDoc. I…
Howard Penrose: Somebody dared tell me that there’s no standard for it. There’s ISO standards for it. Yes. There’s IEEE 1415- Yes … which I chair. Uh, and there’s other standards coming out- This is- … associated with it. And there’s a document that I also chair for Sea Gray- Called A178, which is the practical application of the technology.
So it’s well-documented. There are traceable standards for it. I need more
Allen Hall: operators to call you- Yeah … and to talk to you and get systems in the back of the trucks that they can use to check out the health of their gear boxes and their drive trains and their generators. How [00:24:00] do they do that? Where do they go?
Where, where’s, what’s- Well- … the first place they should look for?
Howard Penrose: Uh, info@motordoc.com. Okay. I get all, I get all of those as well, so do my people. Um, or, uh, LinkedIn. LinkedIn’s really good.
Allen Hall: Look up anything. Yeah.
Howard Penrose: Yeah, yeah. So, so either the company at Motordoc, or, uh, I’m, I sh- I’ll show up either searching for my name or, uh, linkedin.com/in/motordoc.
Come straight to me ’cause I’ve been in, on LinkedIn forever, so- Right, just- … I got to do that … look up
Allen Hall: Howard Penrose, P-E-N-R-O-S-E. Yep. Or go to motordoc.com is- Yep, motordoc.com … the website address.
Howard Penrose: Yep. There’s a lot of great information there. And we have partners, and we have people. We’re growing the company.
You know, talk to me. I, I’ll- Yes … I like answering the phone and talking. It’s, it’s a thing. My people go, “Can we answer the phone one?” No. Um, but, but yeah, we, we, y- when you call us, you’re not just dealing with a single person. Right. The Motordoc is far more expansive. Right now, we [00:25:00] just got our partnership with, uh, Hitachi and, and Juliet- Yeah, that’s great
and stuff like that. Uh, we’re helping them with certain things. Uh, we’re partnered with some of the big OEMs, almost all of them, um, you know, helping identify the issues, you know. And, and when users contact us, often they’ll tell us what’s going on, and we’ll, we can, uh, sometimes say, “Yeah, it’s this, and here’s how we prove it.”
Allen Hall: Yeah. That’s the, that’s the beauty- Yeah … of calling Motordoc. So I need my operators that, that watch the show- Yeah … worldwide, go online, go on LinkedIn, get ahold of Howard, get ahold of Motordoc, and get started. Yep. Howard, thank you- And- … so much for being on the podcast. Yeah. This is fantastic. I love talking to you because-
it’s, it’s like talking to, you know… Uh, no, really, it’s talking like someone who’s a real good industry expert, who’s been there a long time, and understands- Yeah … how this
[00:26:00] works.
Renewable Energy
How Would You Feel If . . .
Sad that it came to this, but not nearly as sad as I would if he gets away with all this criminality, and the rest of the world writes us of as avid supporters of crime and apathetic to rule of law.
Renewable Energy
JD Vance and His Certainty Re: God’s Work
At left we see illustrated the core problem with the world’s theistic religions, i.e., that adherents are so certain of the rightness of their beliefs that they have no problem destroying the world.
FWIW, I have a friend who takes this even further: he is actively rooting for the apocalypse, and the prospect that all believers in God’s son Jesus will be raptured up to heaven to sit at the right hand of God.
He knows that I don’t believe any of this, but he remains confident that I’ll come around. As he told me, “God melts even the hardest of hearts.” He thinks I’m doing the devil’s work with my compassion for others and the effort I put out to achieve a peaceful and sustainable civilization.
We’d be living in tough times even without these fanatics, but religious dogma, especially of this sort, makes it all that much more difficult.
-
Climate Change1 year ago
Guest post: Why China is still building new coal – and when it might stop
-
Greenhouse Gases2 years ago嘉宾来稿:满足中国增长的用电需求 光伏加储能“比新建煤电更实惠”
-
Greenhouse Gases1 year ago
Guest post: Why China is still building new coal – and when it might stop
-
Climate Change2 years ago嘉宾来稿:满足中国增长的用电需求 光伏加储能“比新建煤电更实惠”
-
Renewable Energy10 months agoSending Progressive Philanthropist George Soros to Prison?
-
Climate Change2 years ago
Bill Discounting Climate Change in Florida’s Energy Policy Awaits DeSantis’ Approval
-
Greenhouse Gases1 year ago
嘉宾来稿:探究火山喷发如何影响气候预测
-
Carbon Footprint2 years agoUS SEC’s Climate Disclosure Rules Spur Renewed Interest in Carbon Credits


.png)
.png)
.png)



.png)