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raybot
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So pleased I managed to pick up another 10k, use these opportunities well peeps ( if you can)
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Let's Talk Electronics: MikroE and BrainChip on remote development and neuromorphic computing
24 March 2023
Let's Talk Electronics: MikroE and BrainChip on remote development and neuromorphic computing | Electronics Weekly
Electronics Weekly's Group Editor, Clive Couldwell, caught up with MikroE and BrainChip in Nuremberg at Embedded World 2023 to discuss their technologies in more depth. With Nandan Nayampally - CMO of BrainChip - he discussed neuromorphic computing, how it works and possible applications. And...
Posted 4 days ago.
perceptron
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Fact Finder
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Okay:
Peter van der Made
Peter van der Made
Fact Finder
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Yes perhaps you only now need a Masters degree in Engineering with a High Distinction in Neuro Science and not a PhD to program it.
cosors
👀
I felt that way four times during the pandemic. Time blurred.
____
For once it wasn't my fault. I had work piled on top of each other and had calculated exactly what I was doing on which day of the week. Then my brother on the phone: He explained to me that we have Wednesday instead of Tuesday and my heart went into my pants, how am I going to make this work! One day off, where are my 24h! He was absolutely convincing and since just in the conversation I didn't checked it, I trust my brother or all my siblings. I in a panic the next morning I instructed all. Their reaction unisono: we find your breefing for tomorrow absolutely super. Would be nice if it is always like this. Then I relaized that I had not verified my brother's statement the day before.
All good and people made happy. But what happened to you I can understand very well even if it was with me rather with the pandemic and the blurred situation.
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supersonic001
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Intel Corporation on LinkedIn: Today, we lost a visionary. Gordon Moore leaves us with a legacy of… | 727 comments
Today, we lost a visionary. Gordon Moore leaves us with a legacy of innovation, both as Intel’s co-founder and the author of Moore’s Law. Thank you for… | 727 comments on LinkedIn
Interesting maybe
any thoughts here 
Tothemoon24
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Nice read about predictive maintenance.
kenovy.com
Published on:March 23, 2022
Machines play a huge role in our lives, including the machines we use every day, but without maintenance, every machine will eventually break down. Companies follow various maintenance programs to increase operational reliability and reduce costs.
According to research conducted by Deloitte, a non-optimized maintenance strategy can reduce the production capacity of an industrial plant by 5 to 20%. Recent studies also show that downtime costs industrial manufacturers about 45 billion euros a year.
Not all maintenance activities are the same. Traditionally there are two types of maintenance, corrective (or reactive) and preventive.
Occasionally, corrective maintenance can be also considered a scheduled preventive maintenance task that is performed to correct an existing problem with a machine, system, or component. It is distinguished from preventive maintenance in that the corrective maintenance task is not planned beforehand but arises due to some fault or problem detected in the system.
Consider the case of the industrial internet of things (IIoT), which is a network of devices and sensors that collect and share data to enable companies to predict when equipment will fail, schedule preventive maintenance, and avoid unplanned downtime. This is an ever-growing market that is expected to be worth $225 billion by 2025. There is no doubt that predictive maintenance is an essential application of the IIoT.
Types of Maintenance – Source Splunk
Typically, a machine learning approach is commonly used to define the current state of a system and to predict its future state. It uses data to predict when a machine will break down before it actually breaks down. The development, management and governance of machine learning models is essential for the success of this type of maintenance.
Artificial intelligence is able to process large amounts of complex data in a very short time, which is why many decisions are now delegated to machines. Extensive data is collected and processed (in real-time) along the value chain, which can then be used to analyze the current situation and redefine the desired situation. In this context, it is important for companies to define which data is relevant, in relation to the technology used. The next step is to find and integrate the appropriate measurement tool to capture the values, before defining a model or algorithm suitable for data collection and processing. In this context, it is also important to understand that all stages of the value chain influence each other, which is why an isolated approach is not useful in most cases.
| You might also like to read Model Operations for Secure and Reliable AI
Developing a predictive maintenance machine learning model is not an easy task. It requires a lot of time, effort and resources. A possible high-level strategy could be broken up into these five phases:
Therefore, once we have defined the specific problem to be solved by the predictive approach, we need to set up data acquisition and storage, which is done by installing sensors on the machinery. The next step is to set up an optimization function that will be used for training and testing the model. Finally, you need to train and test your predictive maintenance machine learning model using samples from your data set.
Having all the data collected will give you the possibility to develop a robust algorithm to detect faults more effectively. In some cases, you may not have enough data to represent a healthy and faulty machine, so what you can do is build a mathematical model of the machine and estimate its parameters based on the sensor data. Since the model represents a simulation of the real-world system, in order to model the reliability of the machine, one needs to simulate it with different fault states under different operating conditions. This can be done also by using a failure data generator.
Therefore, we have generated the supplementary data to integrate to that of the sensor; at this point, you can use a combination of both to develop your algorithm. However, once we have the “raw” dataset, it makes sense to remove the outliers and perform a data-cleaning task, for example by filtering out the noise or other useless data. This stage consists of an additional pre-processing step that is often required to reveal additional information that may not be evident in the original form of the data. The data pre-processing stage in addition to the removal of outliers and missing values also consists of advanced signal processing techniques such as short-time Fourier transforms and order domain transformations. For example, it might be useful to convert data from the time domain to the frequency domain to capture useful features condition indicators that are functional to the ability of the ML model to distinguish healthy from faulty conditions.
| You might also like to read Unlocking the Value of AI in Business Applications with ModelOps
The following macro stage is the identification of conditions, which allows us to train machine learning models that is the process at the heart of the predictive maintenance algorithm.
As part of this stage, it is possible to detect anomalies, to train a classifier to identify different types of faults, to gain insights into what part of the machine, equipment or component need to be repaired, and to predict the trend the machine is likely to follow on its path to transition between the states.
Being able to develop a model that captures the relationship between the extracted characteristics and the degradation path of the machine or one of its components, will help you to estimate how much time is left to failure and when you should schedule maintenance.
Then, once the predictive model has been developed, it must be operationalized, distributed on the cloud or on the edge devices (edge computing).
Model operationalising isn’t a trivial process and often represents a challenge for many companies. It is estimated that every single day, companies spend millions of dollars on data scientists, software engineers and artificial intelligence applications. Most of these companies do not have a strategy to make the most of their investment and therefore fail to make a profit. It means that most of the work will never be seen, i.e., the models will either not be put into operation or if delivered (often late) will not render or will not produce the desired results.
So how can we be sure that the models developed for our purposes can effectively perform within established thresholds? How can we manage the entire model lifecycle by setting up a strategy to monitor all models in production and deliver them quickly to achieve business purposes?
DevOps-inspired ModelOps practices ensure regulatory compliance, security and manageability, allowing for continuous delivery as well as smooth and efficient development and deployment of models at scale. ModelOps is important for predictive analytics at scale.
For this reason by setting up a ModelOps process for your machine learning models in order to have in place a predictive maintenance process, your factory can benefit from a full end-to-end AI/ML lifecycle that optimizes your data and AI investments. This practice will allow you to streamline and accelerate data collection and management, model development, model validation, and model deployment. ModelOps is indeed a key capability essential to improve, simplify and automate AI/ML operations and lifecycle management.
ModelOps platforms such as ModelOp Center automate all aspects of model operations, regardless of the model type, how it is developed, or where it is run. The benefits of automation are numerous, such as improving decision-making to achieve faster lead times and maximize production rates, reducing personnel costs, and capturing data that would otherwise be lost through manual methods.
An example of an Enterprise ModelOps Platform can be seen in the following schema.
Source www.modelop.com
| You might like to read also How ModelOps Helps You Execute Your AI Strategy
ModelOp Center v3.0 is the leading ModelOps platform designed to address the challenges that enterprises face in scaling and governing AI programs. ModelOp Center 3.0 introduce three new targeted solutions to address the specific concerns of leaders across the organization.
Source www.modelop.com
We believe the factory, and the personnel responsible for its operations as well as the operation of the single machine must acquire the necessary skills in order to develop and manage AI for production. Knowing at what stage a potential problem may arise is certainly within the capabilities of the machine manufacturer or whoever operates the machine. The data scientists can certainly develop well-crafted models, but they lack the knowledge of the machinery or the single component in general. It is therefore imperative that even if the model is effective at zero time, it can be managed, trained, and adapted via periodic evaluation and maintenance to keep up with changing expectations, circumstances, production capacities, and business objectives.
Having an Enterprise ModelOps platform in place definitely reduces the cognitive effort, the time, and the cost associated with developing and managing industrial AI / ML models.
We discussed why predictive maintenance is important and what steps you need to follow to develop AI models that can pinpoint problems in your machinery and let you know in advance about a future failure.
Implementing an operational framework for analytic models, including AI and ML, offers a number of benefits. One important aspect of this is that it enables manufacturers to build and deploy analytic models with any tool of their choice, automate ongoing monitoring, management, and governance of those models, regardless of where they are running, and integrate those models with the necessary systems and applications.
Through this agnostic approach, an Enterprise ModelOps platform simplifies the adoption of, for example, digital twins and enables faster deployment of analytic models by automating and orchestrating the entire model production life cycle.
Predictive maintenance in industry 4.0: applications and advantages › Kenovy
Machines play a huge role in our lives, including the machines we use every day, but without maintenance, every machine will eventually break down. Companies follow various maintenance programs to increase operational reliability and reduce costs. Maintenance strategy and methods Maintenance is...
kenovy.com
Predictive maintenance in industry 4.0: applications and advantages
AuthorGiuliano LiguoriPublished on:March 23, 2022
Machines play a huge role in our lives, including the machines we use every day, but without maintenance, every machine will eventually break down. Companies follow various maintenance programs to increase operational reliability and reduce costs.
Maintenance strategy and methods
Maintenance is the set of operations necessary to preserve the functionality and efficiency of an asset and can take place in response to a failure or as a previously planned action.According to research conducted by Deloitte, a non-optimized maintenance strategy can reduce the production capacity of an industrial plant by 5 to 20%. Recent studies also show that downtime costs industrial manufacturers about 45 billion euros a year.
Not all maintenance activities are the same. Traditionally there are two types of maintenance, corrective (or reactive) and preventive.
In the case of corrective maintenance, an object is used until a failure occurs. This type of approach is reactive since it is a consequence of an event. Corrective maintenance allows maximum exploitation of the equipment but at the same time implies unreliability, as the exact time of failure is not known and this could cause safety problems. It is clear that unforeseen failures or problems cause temporal as well as considerable economic damage. Corrective maintenance is often more expensive and time-consuming than Preventive maintenance but is necessary to correct problems that have already occurred, it is actually considered the most expensive type of maintenance.
Occasionally, corrective maintenance can be also considered a scheduled preventive maintenance task that is performed to correct an existing problem with a machine, system, or component. It is distinguished from preventive maintenance in that the corrective maintenance task is not planned beforehand but arises due to some fault or problem detected in the system.
In addition to these two maintenance methods, in recent years, with the advent of increasingly powerful and effective innovative technologies, another type of maintenance has made its way, the so-called predictive maintenance. Therefore, let’s explain what predictive maintenance is in order to better understand it.
Predictive maintenance is a key element in the modern manufacturing process. It can be used in industries such as automotive, aerospace, energy, and manufacturing. Predictive maintenance can help companies save money by reducing downtime and preventing equipment failures, it helps companies improve their customer service by allowing them to plan for equipment failures. This kind of maintenance, on the other hand, aims to predict future adverse events, in order to better schedule maintenance.
Consider the case of the industrial internet of things (IIoT), which is a network of devices and sensors that collect and share data to enable companies to predict when equipment will fail, schedule preventive maintenance, and avoid unplanned downtime. This is an ever-growing market that is expected to be worth $225 billion by 2025. There is no doubt that predictive maintenance is an essential application of the IIoT.
By predicting failures before they happen, companies can avoid the cost and inconvenience of downtime. Predictive maintenance also helps companies optimize their resources by scheduling preventive maintenance at times when it will have the least impact on production.
Types of Maintenance – Source Splunk
Algorithms for Monitoring and Predictive Maintenance
As we said, if you can predict when a machine breakdown will occur, you can schedule maintenance right in advance. The good news is that predictive maintenance lets you estimate time to failure. It also pinpoints problems in your complex machinery and helps you identify what parts need to be fixed. By diagnosing or predicting failures, you can plan maintenance in advance, manage inventory more efficiently, reduce downtime, and increase productivity.Typically, a machine learning approach is commonly used to define the current state of a system and to predict its future state. It uses data to predict when a machine will break down before it actually breaks down. The development, management and governance of machine learning models is essential for the success of this type of maintenance.
Artificial intelligence is able to process large amounts of complex data in a very short time, which is why many decisions are now delegated to machines. Extensive data is collected and processed (in real-time) along the value chain, which can then be used to analyze the current situation and redefine the desired situation. In this context, it is important for companies to define which data is relevant, in relation to the technology used. The next step is to find and integrate the appropriate measurement tool to capture the values, before defining a model or algorithm suitable for data collection and processing. In this context, it is also important to understand that all stages of the value chain influence each other, which is why an isolated approach is not useful in most cases.
| You might also like to read Model Operations for Secure and Reliable AI
Developing a predictive maintenance machine learning model is not an easy task. It requires a lot of time, effort and resources. A possible high-level strategy could be broken up into these five phases:
- Define the problem — identify the need for predictive maintenance and define the specific problem to be solved by the predictive maintenance machine learning model.
- Collect data — collect relevant data from different sources that can help in solving the problem.
- Prepare data — prepare all of the collected data into a usable format.
- Analyze data — analyze all of the prepared data to identify patterns and relationships that may be useful in predicting future events or occurrences.
- Develop algorithm or machine learning models — develop algorithms or machine learning models based on analyses conducted in step 4 above.
Therefore, once we have defined the specific problem to be solved by the predictive approach, we need to set up data acquisition and storage, which is done by installing sensors on the machinery. The next step is to set up an optimization function that will be used for training and testing the model. Finally, you need to train and test your predictive maintenance machine learning model using samples from your data set.
Workflow for ML model development
To collect a large set of sensor data representing healthy and faulty operations. You also want to make sure that you collect this data under different operating conditions. For example, the same machine may run in two different places, one in North Europe and one in China. The two machines may operate in different operating conditions, so that, despite having the same type of machine, one may fail sooner than the other.Having all the data collected will give you the possibility to develop a robust algorithm to detect faults more effectively. In some cases, you may not have enough data to represent a healthy and faulty machine, so what you can do is build a mathematical model of the machine and estimate its parameters based on the sensor data. Since the model represents a simulation of the real-world system, in order to model the reliability of the machine, one needs to simulate it with different fault states under different operating conditions. This can be done also by using a failure data generator.
Therefore, we have generated the supplementary data to integrate to that of the sensor; at this point, you can use a combination of both to develop your algorithm. However, once we have the “raw” dataset, it makes sense to remove the outliers and perform a data-cleaning task, for example by filtering out the noise or other useless data. This stage consists of an additional pre-processing step that is often required to reveal additional information that may not be evident in the original form of the data. The data pre-processing stage in addition to the removal of outliers and missing values also consists of advanced signal processing techniques such as short-time Fourier transforms and order domain transformations. For example, it might be useful to convert data from the time domain to the frequency domain to capture useful features condition indicators that are functional to the ability of the ML model to distinguish healthy from faulty conditions.
| You might also like to read Unlocking the Value of AI in Business Applications with ModelOps
The following macro stage is the identification of conditions, which allows us to train machine learning models that is the process at the heart of the predictive maintenance algorithm.
As part of this stage, it is possible to detect anomalies, to train a classifier to identify different types of faults, to gain insights into what part of the machine, equipment or component need to be repaired, and to predict the trend the machine is likely to follow on its path to transition between the states.
Being able to develop a model that captures the relationship between the extracted characteristics and the degradation path of the machine or one of its components, will help you to estimate how much time is left to failure and when you should schedule maintenance.
Then, once the predictive model has been developed, it must be operationalized, distributed on the cloud or on the edge devices (edge computing).
Model operationalising isn’t a trivial process and often represents a challenge for many companies. It is estimated that every single day, companies spend millions of dollars on data scientists, software engineers and artificial intelligence applications. Most of these companies do not have a strategy to make the most of their investment and therefore fail to make a profit. It means that most of the work will never be seen, i.e., the models will either not be put into operation or if delivered (often late) will not render or will not produce the desired results.
So how can we be sure that the models developed for our purposes can effectively perform within established thresholds? How can we manage the entire model lifecycle by setting up a strategy to monitor all models in production and deliver them quickly to achieve business purposes?
ModelOps: A key capability to scale and govern AI initiatives
ModelOps is undoubtedly an organizational capability that enables large enterprises to scale and govern their AI initiatives. More precisely, Enterprise ModelOps proves to be essential for large, complex enterprises with broad plans to leverage models across their operations, while ModelOps is required in some form by any organization.DevOps-inspired ModelOps practices ensure regulatory compliance, security and manageability, allowing for continuous delivery as well as smooth and efficient development and deployment of models at scale. ModelOps is important for predictive analytics at scale.
For this reason by setting up a ModelOps process for your machine learning models in order to have in place a predictive maintenance process, your factory can benefit from a full end-to-end AI/ML lifecycle that optimizes your data and AI investments. This practice will allow you to streamline and accelerate data collection and management, model development, model validation, and model deployment. ModelOps is indeed a key capability essential to improve, simplify and automate AI/ML operations and lifecycle management.
ModelOps platforms such as ModelOp Center automate all aspects of model operations, regardless of the model type, how it is developed, or where it is run. The benefits of automation are numerous, such as improving decision-making to achieve faster lead times and maximize production rates, reducing personnel costs, and capturing data that would otherwise be lost through manual methods.
An example of an Enterprise ModelOps Platform can be seen in the following schema.
Source www.modelop.com
| You might like to read also How ModelOps Helps You Execute Your AI Strategy
ModelOp Center v3.0 is the leading ModelOps platform designed to address the challenges that enterprises face in scaling and governing AI programs. ModelOp Center 3.0 introduce three new targeted solutions to address the specific concerns of leaders across the organization.
Source www.modelop.com
- Executive Visibility for AI solution gives senior executives across the organization a view into where are all AI models running across the enterprise, what business value are they actually driving and are there any operational risk concerns.
- Model Risk Industrialization is a solution designed for risk and compliance teams, to help them to cope with the explosion of the number of models and the actual resource shortage that is occurring for model validators globally.
- AI Orchestration is designed for IT teams to help them to provide consistency standardization and how they onboard and operate models regardless of where models are created. The technologies used in the data science platforms are where those models need to run on-prem in the cloud in a vendor environment.
We believe the factory, and the personnel responsible for its operations as well as the operation of the single machine must acquire the necessary skills in order to develop and manage AI for production. Knowing at what stage a potential problem may arise is certainly within the capabilities of the machine manufacturer or whoever operates the machine. The data scientists can certainly develop well-crafted models, but they lack the knowledge of the machinery or the single component in general. It is therefore imperative that even if the model is effective at zero time, it can be managed, trained, and adapted via periodic evaluation and maintenance to keep up with changing expectations, circumstances, production capacities, and business objectives.
Having an Enterprise ModelOps platform in place definitely reduces the cognitive effort, the time, and the cost associated with developing and managing industrial AI / ML models.
Conclusion
Predictive maintenance is one of the most important parts of the maintenance process. It is a big challenge for companies to make sure they are not wasting time and money on equipment that needs repairing. With predictive maintenance, companies can start to predict when equipment will break down and take action before it happens.We discussed why predictive maintenance is important and what steps you need to follow to develop AI models that can pinpoint problems in your machinery and let you know in advance about a future failure.
Implementing an operational framework for analytic models, including AI and ML, offers a number of benefits. One important aspect of this is that it enables manufacturers to build and deploy analytic models with any tool of their choice, automate ongoing monitoring, management, and governance of those models, regardless of where they are running, and integrate those models with the necessary systems and applications.
Through this agnostic approach, an Enterprise ModelOps platform simplifies the adoption of, for example, digital twins and enables faster deployment of analytic models by automating and orchestrating the entire model production life cycle.
I am thrilled to share my latest creation with you! I've developed an AI-powered Q&A application that can be used on any website, and I've chosen the Brainchip Inc website for the purposes of this demo.
You can access it through this link: https://tw.zeebot.org/web-crawler/brainchip/.
I know the demo might be a little slow, but please bear with me! This is just a temporary issue and is a result of the early demo/prototype nature of the app. I wanted to share it with you as soon as possible because I'm so excited about what it can do.
As early testers, your feedback is incredibly valuable to me. Please don't hesitate to share your screenshots, comments, and suggestions with me. It will help me make the app better and ensure that it meets your needs as users of my Q&A type application.
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There's no setup required for any company that would like to implement it on their website, or as a live chat, or email responder, or anything else you can imagine! It's like having an extremely knowledge personal assistant, sales, or customer care agent who never sleeps.
It’s only limited by what has been published on the website.
So what are you waiting for? Give my app a try and join me in the excitement of the future!
Best regards,
zeeb0t
You can access it through this link: https://tw.zeebot.org/web-crawler/brainchip/.
I know the demo might be a little slow, but please bear with me! This is just a temporary issue and is a result of the early demo/prototype nature of the app. I wanted to share it with you as soon as possible because I'm so excited about what it can do.
As early testers, your feedback is incredibly valuable to me. Please don't hesitate to share your screenshots, comments, and suggestions with me. It will help me make the app better and ensure that it meets your needs as users of my Q&A type application.
My app crawls any website in full to provide you with accurate and up-to-date information using advanced language AI.
There's no setup required for any company that would like to implement it on their website, or as a live chat, or email responder, or anything else you can imagine! It's like having an extremely knowledge personal assistant, sales, or customer care agent who never sleeps.
It’s only limited by what has been published on the website.
So what are you waiting for? Give my app a try and join me in the excitement of the future!
Best regards,
zeeb0t
Last edited:
buena suerte :-)
BOB Bank of Brainchip
Tothemoon24
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Good morning Esq 111
Hope all is well mate any chance of a Sunday tune?
Something to keep me rolling while exercising
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