Introduces intelligent reliability analysis using MATLAB and AI, focusing on failure analysis and reliability engineering. It covers foundational concepts like reliability fundamentals, measures of reliability such as MTBF and MTTF, and remaining useful lifetime (RUL) estimation techniques. The sources also explore experimental methodologies like accelerated life testing for various electronic components and intelligent modeling approaches using Artificial Neural Networks (ANN), Fuzzy Logic (FL), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for predictive maintenance. The overarching goal is to enable proactive component replacement and reduce electronic waste.
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You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Intelligent-Reliability-Analysis-Using-MATLAB/dp/939068465X?&linkCode=ll1&tag=cvthunderx-20&linkId=921309328ec75ab38227d88e76b8c9bf&language=en_US&ref_=as_li_ss_tl
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Welcome to the deep dive. You know, everywhere you look,
our lives are just run by electronics, from that little
chip tracking your steps on your watch to the huge
networks powering our cities. We just we rely on them completely.
We expect them to work right, consistently, flawlessly. But what
happens when they don't, When a key part fails or
a whole system just stops, the effects can be massive.
(00:21):
Today we're taking a really deep look at intelligent reliability
analysis using matt lab and AI. And this isn't just
about stuff breaking. It's the cutting edge signs of predicting
when things might fail, making sure they last longer, and
using artificial intelligence to build systems we can genuinely depend on.
Our mission here is to explore how these advanced techniques
keep our devices running well, how we can anticipate maybe
(00:44):
even prevent failures, and what all this means for everything
really from product design to well sustainability in the environment.
Our main guide for this journey is the book Intelligent
Reliability Analysis using MATT Lab and AI by doctor Cherry
Pargava and doctor Perdeep Kumar Sharma from twenty twenty one.
It's a really rich resource pulling together computer science, AI
(01:04):
and solid reliability engineering. So let's doubt it.
Speaker 2 (01:08):
Okay, So when we talk about reliability, most people just
think does it work? Simple as that. But engineers, scientists,
they have a much more precise way of looking at it.
What does reliability actually mean in this field? And why
is that precision such a big deal today?
Speaker 3 (01:21):
That's a great starting point. Fundamentally, reliability is defined as
the probability that a product performs its intended function satisfactorily
under specific conditions and for a specified period of time.
Speaker 1 (01:36):
Okay, so probability, conditions and time, not just it works
now exactly.
Speaker 3 (01:40):
It's not just a one off check. It's about sustained
performance under expected stress for a certain duration. This whole
way of thinking really took shape or became critical during
World War II. I think military gear aerospace failure was just.
Speaker 1 (01:56):
Not an option right, high stakes, absolutely.
Speaker 3 (01:58):
And it connects to other ideas too, like survivability can
it keep working even when things go wrong? Or reparability
how easily can we fix it? There's also a longevity maintainability,
and when you combine reliability with maintainability you get availability.
Speaker 1 (02:11):
Availability, Is it ready when I need it?
Speaker 3 (02:13):
Precisely crucial for systems that need to be up and
running almost constantly.
Speaker 1 (02:17):
That distinction, the probability conditions time, it really highlights the
complexity because today, wow, the systems are just so incredibly intricate,
aren't they. Our source mentions a single chip having millions
of transistors, and in those common series connections, if just
one tiny part fails or even just degrades a bit
(02:38):
poof the whole system can shut down. You expect your
phone to work, your car's GPS, the power grid, you
just expect it. That expectation is reliability.
Speaker 3 (02:48):
It is. And to really get a grip on reliability,
you also have to understand well failure the flip side exactly.
Failure is simply when an item stops being able to
do what it's supposed to do. And they're not all
the same. Some failures are due, to say it in error
weakness from the start. Some are sudden catastrophic. Others are
gradual like degradation over time.
Speaker 1 (03:06):
What causes them typically, ah lots of things.
Speaker 3 (03:08):
Poor design choices, maybe a lack of experience in the
design team, bad maintenance practices, wrong manufacturing techniques, even human
error in operation. Engineers often visualize this using something called
the bathtub curve.
Speaker 1 (03:22):
Ah, yes, I've heard of that.
Speaker 3 (03:23):
It sort of shows failure rates. Over time. You get
some early failures, maybe manufacturing defects, the infant mortality phase,
then a long period of relatively low random failures that's
the useful life, and finally, as components age and wear out,
the failure rate starts to climb again the wear out phase.
It's a really useful model.
Speaker 1 (03:43):
So we know what reliability is. We know failures happen
and often follow patterns. But how do we put numbers
on this? How do engineers actually measure it? And then
you know, design systems to be resilient.
Speaker 3 (03:54):
Right, that's where the metrics come in. These are the
key numbers for things you don't usually repair, like maybe
it's specific type of sensor. We use meantime to failure
or MTTF.
Speaker 1 (04:03):
Okay, average time until it breaks.
Speaker 3 (04:05):
Pretty much for systems you can repair, like a complex
machine or a server, we talk about meantime between failures
or MTBF.
Speaker 1 (04:12):
Time between breakdowns. Assuming you fix it each time, yes.
Speaker 3 (04:15):
Assuming a constant failure rate during its useful life. Then
there's the failure rate itself, sometimes called the hazard rate
That tells you how likely a failure is within a
specific time window. Is it going up, down, staying steady?
Speaker 1 (04:27):
And you mentioned availability earlier, right, Availability super important for
repaarable stuff.
Speaker 3 (04:32):
It's the probability the system is actually operational when you
need it. It factors in both how often it breaks down,
its reliability, and how quickly you can get it back online.
It's maintainability, it's uptime.
Speaker 1 (04:44):
Okay. So these numbers MTTF, MTBF, failure rate, availability, they
give engineers concrete targets exactly.
Speaker 3 (04:51):
They move it from a vague concept to something measurable
and achievable in design.
Speaker 1 (04:56):
What's fascinating, then, is how these numbers influence the actual design,
the architecture of a system. It's not just about good parts,
but how you put them together. Right, Let's talk configurations.
The simplest one you mentioned is the series configuration. Sounds risky,
it often is.
Speaker 3 (05:11):
In a pure series setup, every single component has to
work for the whole system to work.
Speaker 1 (05:15):
Like Christmas lights, one bulb goes, the whole string is out.
Speaker 3 (05:18):
That's a classic analogy. The source gives a numerical example,
three independent systems, each ninety percent reliable, put them in series,
and the overall reliability plummets to point nine times point
nine times point nine, which is only seventy two point
nine percent.
Speaker 1 (05:32):
Ouch only as strong as the weakest link.
Speaker 3 (05:34):
Pcisely, but then you have the opposite approach. Parallel configuration
backup systems exactly redundancy. If one path or component fails,
another one is there to take over. Think about critical
systems on an airplane, maybe flight controls. They often have
triple redundancy, three parallel systems.
Speaker 1 (05:51):
So even if two fail, the third keeps things going,
boosts reliability massively immensely.
Speaker 3 (05:57):
And in the real world, of course, most complex system
aren't purely series or purely parallel. They use mixed configurations,
combinations of series and parallel arrangements carefully designed to balance cost, performance,
and that all important reliability.
Speaker 1 (06:12):
Got it So understanding these setups helps explain why some
gadgets seem fragile with single points of failure, while others,
maybe more expensive ones, feel incredibly robust, even if the
core parts aren't that different.
Speaker 3 (06:24):
It's the architecture, absolutely, It's all about those design choices
and how they leverage or mitigate the risks identified by
the reliability metrics.
Speaker 1 (06:32):
Okay, so we've covered the basics, the metrics, the configurations,
solid foundation, But the really exciting part. The game changer
today isn't just measuring reliability after the fact, it's predicting it.
Moving from being reactive fixing things when they break to
being proactive knowing before it breaks. It sounds like you'd
need a crystal ball, but you're saying we have something better, AI,
(06:55):
You've hit.
Speaker 3 (06:55):
The nail on the head. The frontier of reliability engineering
right now is heavily focused on prediction, specifically something called
remaining useful lifetime estimation or.
Speaker 1 (07:06):
R L RUL. Okay, how much life is left.
Speaker 3 (07:09):
In something exactly? Think about it? So many components, perfectly
good components often outlive the gadget they were first put.
Speaker 1 (07:15):
Into, right like parts in an old phone might still
be fun.
Speaker 3 (07:17):
Precisely knowing the RUL is vital for safety, of course,
preventing unexpected failures, but it's also huge for minimizing electronic waste,
a massive environmental issue.
Speaker 1 (07:28):
So are you all helps us reuse things more effectively?
Speaker 3 (07:31):
Absolutely? Historically, trying to predict AREUL involved statistical models, maybe
running experiments, accelerated life testing, or using empirical data like
from military handbooks. But these methods struggle with the sheer
complexity of modern electronics. This is where intelligent models AI
powered models have become well almost essential. They can monitor
(07:52):
systems in real time and make these prognostications that.
Speaker 1 (07:56):
Jump from just looking at past data to actually predicting
the future. That's where the AI magic happens. Our source
talks about a few key AI models. Let's start with
artificial neural networks. Ann's sounds like mimicking the brain in
a way.
Speaker 3 (08:10):
Yes, ANNs are inspired by how our brains learn. You
feed them lots of data, training data showing inputs and
corresponding outputs. The network adjusts itself, learning the underlying patterns
and relationships, even really complex nonlinear ones. Once it's trained,
you give it new input data it hasn't seen before,
and you can predict the likely output. It's forecasting based
on learned experience.
Speaker 1 (08:30):
Okay, learning from data. Then there's fuzzy logic f hel
That sounds less precise.
Speaker 3 (08:34):
Hey, the name is a bit misleading. Perhaps it's actually
very clever for dealing with real world ambiguity. Things aren't
always just black or white, true or false. Fuzzy logic
uses linguistic variables terms like very low, medium, quite high, more.
Speaker 1 (08:49):
Like how humans talk about things exactly.
Speaker 3 (08:53):
It uses a set of rules based on these fuzzy terms,
takes precise input data, makes it fuzzy, applies the rules,
gets a fuzzy output, and then converts that back into
a precise, usable prediction or decision.
Speaker 1 (09:04):
Interesting handling the gray areas. But you said the real
power comes when you combine these.
Speaker 3 (09:09):
Yes, that's where ANFES comes in the adaptive neuro fuzzy
inference system.
Speaker 1 (09:14):
Fuzzy, Okay, that's to both worlds.
Speaker 3 (09:16):
That's the idea. ANFS integrates the learning power of neural
networks with the human like reasoning of fuzzy logic, often
using a specific approach called the pseugenome model. It can
learn the fuzzy rules directly from data, adapting and refining them.
This makes it incredibly powerful and accurate for prediction, especially
in complex situations where the relationships aren't obvious.
Speaker 1 (09:37):
So how does this work in practice? Our source had
an example right with capacitors.
Speaker 3 (09:42):
Yes, a great case study predicting the RUL of an
electrolytic capacitor. These are everywhere in electronics right, common component,
very common, but their lifespan is tricky. It's affected by
lots of interacting factors. Temperature, the voltage, applied ripple current,
something called ESR equivalent series resistance, even humidity.
Speaker 1 (10:01):
Wow, lots of variables.
Speaker 3 (10:02):
Exactly. Predicting failure accurately based on all those interacting factors
used to be really hard. You'd often rely on very
broad estimates. But the study showed nas Mass taking all
these factors into account, could predict the RUL with wait
for it, ninety nine point two eight percent accuracy.
Speaker 1 (10:18):
Ninety nine point two eight. That's incredibly precise.
Speaker 3 (10:21):
It really is. That kind of accuracy changes everything. You
move from just replacing parts on a schedule or waiting
for failure to knowing exactly when maintenance is needed. It
optimizes everything, and.
Speaker 1 (10:31):
Tools like matt lab are crucial here right for building
and testing these AI models absolutely.
Speaker 3 (10:37):
Matt Lab provides the environment where engineers can design, train, validate,
and deploy these complex models like anm fuzzy logic necesarially nfis,
but these reliability tasks, it's the workbench.
Speaker 1 (10:48):
It makes you think. Imagine your car telling you, hey,
this specific part you've got about three thousand miles left
on it. No more surprise breakdowns or being able to
test components from old electronics and know, okay, this one
still has eighty percent of its useful life left, so
it can be reliably reused cutting down eWays.
Speaker 3 (11:08):
That's precisely the potential impact we're talking about.
Speaker 1 (11:10):
This really goes beyond just the TEXTPECS, doesn't it. It
hits environmental issues how we consume things. Let's talk more
about that reuse idea and ewyse.
Speaker 3 (11:18):
Definitely, this reuse philosophy is a direct outcome of better
RUL prediction. As the source points out, many components just
last way longer than the product they were first put.
Speaker 1 (11:28):
In, right, the product gets outdated or something else breaks,
but some parts are still good exactly.
Speaker 3 (11:33):
Think back to that bathtub curve. A product might reach
its wear out phase, maybe due to one key failure
or just obsolescence, but inside individual components, resistors, capacitors, processors
might still be well within their main useful life period.
Speaker 1 (11:47):
Without knowing their RUL, we just toss the whole thing.
Speaker 3 (11:50):
Right, which is a huge waste. By accurately knowing a
component's remaining life, we can confidently reuse it extract its
full value. This is absolutely vital for minimizing e waste,
conserving the energy and resources used to make new parts,
and ultimately creating a greener approach to technology, a more
circular economy.
Speaker 1 (12:10):
That's a really positive angle. Now beyond individual parts, where
else is this kind of reliability analysis making a big difference?
Speaker 3 (12:17):
Well. A key area highlighted in the source is wireless
sensor networks or WSNs.
Speaker 1 (12:22):
AH networks of tiny sensors used for monitoring.
Speaker 3 (12:26):
Things exactly think environmental monitoring, industrial process control, structural health
monitoring for bridges. These networks use lots of small, inexpensive,
low power sensor nodes. But because they are low cost
and often deployed in harsh environments, individual nodes can be
prone to failure, hardware issues, communication problems.
Speaker 1 (12:44):
So how do you make the network reliable If the
nodes aren't individually super.
Speaker 3 (12:48):
Reliable, Redundancy is key. You often deploy many more nodes
than you strictly need. The focus shifts from relying on
any single node to be perfect to getting reliable information
from the collective networ work. Even if some nodes fail,
the overall coverage and data delivery remain robust.
Speaker 1 (13:05):
So network reliability depends on the group, not just the
individuals precisely.
Speaker 3 (13:10):
And it's not just about nodes surviving. It's about the
reliability of the data coverage, the timeliness of data delivery,
the security of the communication, all crucial aspects for WSNs.
Speaker 1 (13:21):
And it seems like this thinking applies way beyond electronics too.
Speaker 3 (13:24):
Oh. Absolutely, the principles are universal in engineering. The source
mentions mechanical reliability designing durable gears, bearings, shafts. There's software
reliability writing code that doesn't crash or behave unexpectedly hugely
important structural reliability and civil engineering ensuring bridges, buildings, dams
are safe over their lifespan. We also see robot reliability
(13:45):
and safety, which is becoming more critical as robots work
alongside humans. And of course, power system reliability keeping the
lights on is fundamental.
Speaker 1 (13:53):
It really is everywhere. Okay, So this deep dive has
taken us quite a journey from just defining reliability and
fail through the metrics and system designs all the way
to this cutting edge AI for predicting the future life
of components. It really shows how fields like computer science,
AI and traditional engineering are coming together to build things
(14:14):
that are not just powerful, but also dependable and more sustainable.
Speaker 3 (14:19):
Absolutely, and ultimately, this intelligent reliability analysis it's not just
about preventing things from breaking down. It's about optimizing how
things perform over their entire life. It helps us make
smarter decisions about everything from how long a warranty should
be to how we manage global e waste. It's truly
transforming how we design, use, and eventually reuse technology, pushing
us towards a more resilient and hopefully sustainable future.
Speaker 1 (14:42):
So here's a final thought for you, our listener. After
digging into all of this, think about your daily life.
What single component maybe your phone's battery, maybe a part
in your car's engine, maybe something else entirely, what single
component would you most want to know the exact remaining
useful lifetime of And how would having that precise not
actually changed the decisions you make? Something to ponder
Welcome to the deep dive. You know, everywhere you look,
our lives are just run by electronics, from that little
chip tracking your steps on your watch to the huge
networks powering our cities. We just we rely on them completely.
We expect them to work right, consistently, flawlessly. But what
happens when they don't, When a key part fails or
a whole system just stops, the effects can be massive.
(00:21):
Today we're taking a really deep look at intelligent reliability
analysis using matt lab and AI. And this isn't just
about stuff breaking. It's the cutting edge signs of predicting
when things might fail, making sure they last longer, and
using artificial intelligence to build systems we can genuinely depend on.
Our mission here is to explore how these advanced techniques
keep our devices running well, how we can anticipate maybe
(00:44):
even prevent failures, and what all this means for everything
really from product design to well sustainability in the environment.
Our main guide for this journey is the book Intelligent
Reliability Analysis using MATT Lab and AI by doctor Cherry
Pargava and doctor Perdeep Kumar Sharma from twenty twenty one.
It's a really rich resource pulling together computer science, AI
(01:04):
and solid reliability engineering. So let's doubt it.
Speaker 2 (01:08):
Okay, So when we talk about reliability, most people just
think does it work? Simple as that. But engineers, scientists,
they have a much more precise way of looking at it.
What does reliability actually mean in this field? And why
is that precision such a big deal today?
Speaker 3 (01:21):
That's a great starting point. Fundamentally, reliability is defined as
the probability that a product performs its intended function satisfactorily
under specific conditions and for a specified period of time.
Speaker 1 (01:36):
Okay, so probability, conditions and time, not just it works
now exactly.
Speaker 3 (01:40):
It's not just a one off check. It's about sustained
performance under expected stress for a certain duration. This whole
way of thinking really took shape or became critical during
World War II. I think military gear aerospace failure was just.
Speaker 1 (01:56):
Not an option right, high stakes, absolutely.
Speaker 3 (01:58):
And it connects to other ideas too, like survivability can
it keep working even when things go wrong? Or reparability
how easily can we fix it? There's also a longevity maintainability,
and when you combine reliability with maintainability you get availability.
Speaker 1 (02:11):
Availability, Is it ready when I need it?
Speaker 3 (02:13):
Precisely crucial for systems that need to be up and
running almost constantly.
Speaker 1 (02:17):
That distinction, the probability conditions time, it really highlights the
complexity because today, wow, the systems are just so incredibly intricate,
aren't they. Our source mentions a single chip having millions
of transistors, and in those common series connections, if just
one tiny part fails or even just degrades a bit
(02:38):
poof the whole system can shut down. You expect your
phone to work, your car's GPS, the power grid, you
just expect it. That expectation is reliability.
Speaker 3 (02:48):
It is. And to really get a grip on reliability,
you also have to understand well failure the flip side exactly.
Failure is simply when an item stops being able to
do what it's supposed to do. And they're not all
the same. Some failures are due, to say it in error
weakness from the start. Some are sudden catastrophic. Others are
gradual like degradation over time.
Speaker 1 (03:06):
What causes them typically, ah lots of things.
Speaker 3 (03:08):
Poor design choices, maybe a lack of experience in the
design team, bad maintenance practices, wrong manufacturing techniques, even human
error in operation. Engineers often visualize this using something called
the bathtub curve.
Speaker 1 (03:22):
Ah, yes, I've heard of that.
Speaker 3 (03:23):
It sort of shows failure rates. Over time. You get
some early failures, maybe manufacturing defects, the infant mortality phase,
then a long period of relatively low random failures that's
the useful life, and finally, as components age and wear out,
the failure rate starts to climb again the wear out phase.
It's a really useful model.
Speaker 1 (03:43):
So we know what reliability is. We know failures happen
and often follow patterns. But how do we put numbers
on this? How do engineers actually measure it? And then
you know, design systems to be resilient.
Speaker 3 (03:54):
Right, that's where the metrics come in. These are the
key numbers for things you don't usually repair, like maybe
it's specific type of sensor. We use meantime to failure
or MTTF.
Speaker 1 (04:03):
Okay, average time until it breaks.
Speaker 3 (04:05):
Pretty much for systems you can repair, like a complex
machine or a server, we talk about meantime between failures
or MTBF.
Speaker 1 (04:12):
Time between breakdowns. Assuming you fix it each time, yes.
Speaker 3 (04:15):
Assuming a constant failure rate during its useful life. Then
there's the failure rate itself, sometimes called the hazard rate
That tells you how likely a failure is within a
specific time window. Is it going up, down, staying steady?
Speaker 1 (04:27):
And you mentioned availability earlier, right, Availability super important for
repaarable stuff.
Speaker 3 (04:32):
It's the probability the system is actually operational when you
need it. It factors in both how often it breaks down,
its reliability, and how quickly you can get it back online.
It's maintainability, it's uptime.
Speaker 1 (04:44):
Okay. So these numbers MTTF, MTBF, failure rate, availability, they
give engineers concrete targets exactly.
Speaker 3 (04:51):
They move it from a vague concept to something measurable
and achievable in design.
Speaker 1 (04:56):
What's fascinating, then, is how these numbers influence the actual design,
the architecture of a system. It's not just about good parts,
but how you put them together. Right, Let's talk configurations.
The simplest one you mentioned is the series configuration. Sounds risky,
it often is.
Speaker 3 (05:11):
In a pure series setup, every single component has to
work for the whole system to work.
Speaker 1 (05:15):
Like Christmas lights, one bulb goes, the whole string is out.
Speaker 3 (05:18):
That's a classic analogy. The source gives a numerical example,
three independent systems, each ninety percent reliable, put them in series,
and the overall reliability plummets to point nine times point
nine times point nine, which is only seventy two point
nine percent.
Speaker 1 (05:32):
Ouch only as strong as the weakest link.
Speaker 3 (05:34):
Pcisely, but then you have the opposite approach. Parallel configuration
backup systems exactly redundancy. If one path or component fails,
another one is there to take over. Think about critical
systems on an airplane, maybe flight controls. They often have
triple redundancy, three parallel systems.
Speaker 1 (05:51):
So even if two fail, the third keeps things going,
boosts reliability massively immensely.
Speaker 3 (05:57):
And in the real world, of course, most complex system
aren't purely series or purely parallel. They use mixed configurations,
combinations of series and parallel arrangements carefully designed to balance cost, performance,
and that all important reliability.
Speaker 1 (06:12):
Got it So understanding these setups helps explain why some
gadgets seem fragile with single points of failure, while others,
maybe more expensive ones, feel incredibly robust, even if the
core parts aren't that different.
Speaker 3 (06:24):
It's the architecture, absolutely, It's all about those design choices
and how they leverage or mitigate the risks identified by
the reliability metrics.
Speaker 1 (06:32):
Okay, so we've covered the basics, the metrics, the configurations,
solid foundation, But the really exciting part. The game changer
today isn't just measuring reliability after the fact, it's predicting it.
Moving from being reactive fixing things when they break to
being proactive knowing before it breaks. It sounds like you'd
need a crystal ball, but you're saying we have something better, AI,
(06:55):
You've hit.
Speaker 3 (06:55):
The nail on the head. The frontier of reliability engineering
right now is heavily focused on prediction, specifically something called
remaining useful lifetime estimation or.
Speaker 1 (07:06):
R L RUL. Okay, how much life is left.
Speaker 3 (07:09):
In something exactly? Think about it? So many components, perfectly
good components often outlive the gadget they were first put.
Speaker 1 (07:15):
Into, right like parts in an old phone might still
be fun.
Speaker 3 (07:17):
Precisely knowing the RUL is vital for safety, of course,
preventing unexpected failures, but it's also huge for minimizing electronic waste,
a massive environmental issue.
Speaker 1 (07:28):
So are you all helps us reuse things more effectively?
Speaker 3 (07:31):
Absolutely? Historically, trying to predict AREUL involved statistical models, maybe
running experiments, accelerated life testing, or using empirical data like
from military handbooks. But these methods struggle with the sheer
complexity of modern electronics. This is where intelligent models AI
powered models have become well almost essential. They can monitor
(07:52):
systems in real time and make these prognostications that.
Speaker 1 (07:56):
Jump from just looking at past data to actually predicting
the future. That's where the AI magic happens. Our source
talks about a few key AI models. Let's start with
artificial neural networks. Ann's sounds like mimicking the brain in
a way.
Speaker 3 (08:10):
Yes, ANNs are inspired by how our brains learn. You
feed them lots of data, training data showing inputs and
corresponding outputs. The network adjusts itself, learning the underlying patterns
and relationships, even really complex nonlinear ones. Once it's trained,
you give it new input data it hasn't seen before,
and you can predict the likely output. It's forecasting based
on learned experience.
Speaker 1 (08:30):
Okay, learning from data. Then there's fuzzy logic f hel
That sounds less precise.
Speaker 3 (08:34):
Hey, the name is a bit misleading. Perhaps it's actually
very clever for dealing with real world ambiguity. Things aren't
always just black or white, true or false. Fuzzy logic
uses linguistic variables terms like very low, medium, quite high, more.
Speaker 1 (08:49):
Like how humans talk about things exactly.
Speaker 3 (08:53):
It uses a set of rules based on these fuzzy terms,
takes precise input data, makes it fuzzy, applies the rules,
gets a fuzzy output, and then converts that back into
a precise, usable prediction or decision.
Speaker 1 (09:04):
Interesting handling the gray areas. But you said the real
power comes when you combine these.
Speaker 3 (09:09):
Yes, that's where ANFES comes in the adaptive neuro fuzzy
inference system.
Speaker 1 (09:14):
Fuzzy, Okay, that's to both worlds.
Speaker 3 (09:16):
That's the idea. ANFS integrates the learning power of neural
networks with the human like reasoning of fuzzy logic, often
using a specific approach called the pseugenome model. It can
learn the fuzzy rules directly from data, adapting and refining them.
This makes it incredibly powerful and accurate for prediction, especially
in complex situations where the relationships aren't obvious.
Speaker 1 (09:37):
So how does this work in practice? Our source had
an example right with capacitors.
Speaker 3 (09:42):
Yes, a great case study predicting the RUL of an
electrolytic capacitor. These are everywhere in electronics right, common component,
very common, but their lifespan is tricky. It's affected by
lots of interacting factors. Temperature, the voltage, applied ripple current,
something called ESR equivalent series resistance, even humidity.
Speaker 1 (10:01):
Wow, lots of variables.
Speaker 3 (10:02):
Exactly. Predicting failure accurately based on all those interacting factors
used to be really hard. You'd often rely on very
broad estimates. But the study showed nas Mass taking all
these factors into account, could predict the RUL with wait
for it, ninety nine point two eight percent accuracy.
Speaker 1 (10:18):
Ninety nine point two eight. That's incredibly precise.
Speaker 3 (10:21):
It really is. That kind of accuracy changes everything. You
move from just replacing parts on a schedule or waiting
for failure to knowing exactly when maintenance is needed. It
optimizes everything, and.
Speaker 1 (10:31):
Tools like matt lab are crucial here right for building
and testing these AI models absolutely.
Speaker 3 (10:37):
Matt Lab provides the environment where engineers can design, train, validate,
and deploy these complex models like anm fuzzy logic necesarially nfis,
but these reliability tasks, it's the workbench.
Speaker 1 (10:48):
It makes you think. Imagine your car telling you, hey,
this specific part you've got about three thousand miles left
on it. No more surprise breakdowns or being able to
test components from old electronics and know, okay, this one
still has eighty percent of its useful life left, so
it can be reliably reused cutting down eWays.
Speaker 3 (11:08):
That's precisely the potential impact we're talking about.
Speaker 1 (11:10):
This really goes beyond just the TEXTPECS, doesn't it. It
hits environmental issues how we consume things. Let's talk more
about that reuse idea and ewyse.
Speaker 3 (11:18):
Definitely, this reuse philosophy is a direct outcome of better
RUL prediction. As the source points out, many components just
last way longer than the product they were first put.
Speaker 1 (11:28):
In, right, the product gets outdated or something else breaks,
but some parts are still good exactly.
Speaker 3 (11:33):
Think back to that bathtub curve. A product might reach
its wear out phase, maybe due to one key failure
or just obsolescence, but inside individual components, resistors, capacitors, processors
might still be well within their main useful life period.
Speaker 1 (11:47):
Without knowing their RUL, we just toss the whole thing.
Speaker 3 (11:50):
Right, which is a huge waste. By accurately knowing a
component's remaining life, we can confidently reuse it extract its
full value. This is absolutely vital for minimizing e waste,
conserving the energy and resources used to make new parts,
and ultimately creating a greener approach to technology, a more
circular economy.
Speaker 1 (12:10):
That's a really positive angle. Now beyond individual parts, where
else is this kind of reliability analysis making a big difference?
Speaker 3 (12:17):
Well. A key area highlighted in the source is wireless
sensor networks or WSNs.
Speaker 1 (12:22):
AH networks of tiny sensors used for monitoring.
Speaker 3 (12:26):
Things exactly think environmental monitoring, industrial process control, structural health
monitoring for bridges. These networks use lots of small, inexpensive,
low power sensor nodes. But because they are low cost
and often deployed in harsh environments, individual nodes can be
prone to failure, hardware issues, communication problems.
Speaker 1 (12:44):
So how do you make the network reliable If the
nodes aren't individually super.
Speaker 3 (12:48):
Reliable, Redundancy is key. You often deploy many more nodes
than you strictly need. The focus shifts from relying on
any single node to be perfect to getting reliable information
from the collective networ work. Even if some nodes fail,
the overall coverage and data delivery remain robust.
Speaker 1 (13:05):
So network reliability depends on the group, not just the
individuals precisely.
Speaker 3 (13:10):
And it's not just about nodes surviving. It's about the
reliability of the data coverage, the timeliness of data delivery,
the security of the communication, all crucial aspects for WSNs.
Speaker 1 (13:21):
And it seems like this thinking applies way beyond electronics too.
Speaker 3 (13:24):
Oh. Absolutely, the principles are universal in engineering. The source
mentions mechanical reliability designing durable gears, bearings, shafts. There's software
reliability writing code that doesn't crash or behave unexpectedly hugely
important structural reliability and civil engineering ensuring bridges, buildings, dams
are safe over their lifespan. We also see robot reliability
(13:45):
and safety, which is becoming more critical as robots work
alongside humans. And of course, power system reliability keeping the
lights on is fundamental.
Speaker 1 (13:53):
It really is everywhere. Okay, So this deep dive has
taken us quite a journey from just defining reliability and
fail through the metrics and system designs all the way
to this cutting edge AI for predicting the future life
of components. It really shows how fields like computer science,
AI and traditional engineering are coming together to build things
(14:14):
that are not just powerful, but also dependable and more sustainable.
Speaker 3 (14:19):
Absolutely, and ultimately, this intelligent reliability analysis it's not just
about preventing things from breaking down. It's about optimizing how
things perform over their entire life. It helps us make
smarter decisions about everything from how long a warranty should
be to how we manage global e waste. It's truly
transforming how we design, use, and eventually reuse technology, pushing
us towards a more resilient and hopefully sustainable future.
Speaker 1 (14:42):
So here's a final thought for you, our listener. After
digging into all of this, think about your daily life.
What single component maybe your phone's battery, maybe a part
in your car's engine, maybe something else entirely, what single
component would you most want to know the exact remaining
useful lifetime of And how would having that precise not
actually changed the decisions you make? Something to ponder
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