How can you use artificial intelligence to improve predictive maintenance in industry?

In a nutshell: AI's integration into predictive maintenance is revolutionizing asset management. AI allows industries to analyze vast data sets from equipment sensors in real time, enabling the early detection of potential failures. This proactive approach significantly reduces downtime and maintenance costs. Moreover, AI algorithms can learn from historical data, improving their predictive accuracy. This improves operational efficiency and extends the assets' lifespan, ensuring a more sustainable and cost-effective production environment.

Predictive Maintenance isn't predictive without leveraging AI in one way or another. Why?.... One of the real reasons why these new processes are necessary is the massive loss of industrial knowledge that is leaving modern factories due to previous generations retiring. They could analyze by sound, feel, or even smell problems that may occur using their 40 years of working in the same factory. Today's solution to this is using AI. Analyzing vast amounts of factory data from every available and different data source, to identify patterns that lead to a positive or negative outcome. For example; When the weather was X, humidity was Y, Vibration was Z, heat was W... The machine's failure rate was higher.

Las herramientas tradicionales de automatización y adquisición de datos en procesos industriales permiten generar reportes muy completos para desarrollar planes de mantenimiento preventivo, sin embargo el análisis y aplicación de los datos puede ser una gran debilidad. Ahora con las herramientas de IA los datos que brinda el proceso pueden ser analizados de una manera más clara, la habilidad de interpretar correlaciones entre variables permite enfocar todos los esfuerzos en aspectos específicos ahorrando tiempo y dinero.

AI is not a silver bullet and in its current form enhances our human ability to make better decisions. Here are a few examples of its application: 1) It can be used to analyze historical machine and sensor data to predict equipment failure based on a known baseline of optimal operating conditions. 2) Real-time monitoring and early fault detection enabling operators and supervisors to focus their attention and prioritize maintenance interventions, reducing cost and preventing unplanned downtime. 3) Support during a critical event to determine the root cause of an incident and offer troubleshooting steps to reduce the mean time to repair.

AI for predictive maintenance is a great idea as it should eliminate the need to have maint resource on site waiting to repair to failure. Monitoring and learning trends of running to failure of devices starting from MTTF and MTBF values has it's benefits and should only be more accurate as the algorithm grows.

AI has generally been found to not be particularly accurate for predictive maintenance. It has been good for anomaly detection (changes to expected values) and classification (through machine learning). The true use of AI is the use of data for identification of opportunities within the system.

My experience is that many just don’t want to put in the work and research on their machines so AI can do more then just look at patterns. But that had been an issue with Vibration Analysis for decades. People never seem to want to build out solid foundations to work from. Hopefully some day I will find someone that wants to try things I have been asking for for years. I feel it can do much more but you need to find someone willing to push the bar higher instead of just leaning on it.

It somewhat depends on how you define predictive maintenance. If you are looking at historical data to make a prediction of when an asset may fail, then yes. But so did basic SPC 30 years ago. To that end, it isn't reliable. Look at failure progression curves for the reasons why. If you define it as Condition Monitoring to identify an existing failure mode, then not only no, but it is somewhat silly. These are known math equations if you have the domain knowledge. With all of that said ML does have a great use case in our industry. This would be to identify the cause of point "P" for purposes of eliminating it. This use case requires a tremendous amounts of data of different types and there is no known equations or correlations.

Application of artificial intelligence in predictive maintenance (PM) not only enhances the reliability and performance of equipment but also brings about operational efficiencies and cost savings for businesses. below mentioned are the list of benefits if Artificial intelligence is applied in PM:- 1. Reduced Equipment Downtime 2. Improved Equipment Efficiency 3. Condition Based Monitoring 4. Data-Driven Insights 5. Predictive Analytics 6. Extended Equipment Lifespan 7. Improved Overall Equipment Effectiveness (OEE)

I couldn't agree with Mr. Penrose more regarding the accuracy of predictive maintenance. Critically important to have qualified and experienced human analysts interpreting and analyzing data. Combining multiple technologies to assist in making informed decisions. We need experienced humans, coupled with science to build credible AI models.

I have been working with different AI strategies for the last few years, trying to build a rich data set specific to AC drives and the set of agnostic 16 parameters common across brands. The challenge with implementation is, indeed, the data and the impending search for a hypothesis. What I assume may be the correct combination of factors contributing to a premature failure for a general-purpose drive running a PD pump is not what the data indicates. Sometimes, the direction is quite interesting.

This is a strong point. The fact that an AI engine cannot always articulate the reasons that a prediction is made lowers the confidence in that prediction. Humans are good at answering this question most of the time, but there are other occasions where a strong expert recommends a course of action based on "intuition" because they have seen similar cases. These black box items are more like that expert we trust, difference is we trust the expert from experience, we don't know the black box because it learned so fast so we need proof.

AI does not have to be black-box models. Using tree models like random forest is usually accurate, robust, and explainable. Data preparation and feature engineering are, as usual, the most important elements in using AI. Some of the biggest challenges for AI models in my estimation would be: - Concept drift: models may become innacurate over time due to changing environments drifting outside of training parameters. - data scarcity: you generally have data for suspended parts, not failed parts. And few samples with many parameters. This is compounded with... - the curse of dimensionality plus poor feature selection/engineering

One specific challenge is around the location and ownership of the data required to build ML models. Access to more data means more differentiated and performant models, increasing accuracy. However access to this data can be difficult if across multiple sites or regions, in addition this data can also be spread across a number of organisations including manufacturers, operators and the model owner. To add to the challenge, the data and the model can also be regarded as intellectual property, making sharing and collaboration difficult. Solutions that leverage Federated Machine Learning and Computational Governance can allow for ML models to be trained and run on data where it resides, removing the need to centralise and protecting IP.

Using field instruments will be a key point in this topic by collecting more data about the process and the current conditions, which will help feed a predictive AI model to define when maintenance is required to avoid process downtime. However, such a model needs more than just process data to predict maintenance; it may require more data, like the last time of maintenance or the last time of failure.

There are wide variety of sensors available which can do wonders for predictive maintenance along with AI. e.g A vibration sensor can pass data to model and based on analysis by AI, a maintenance notification can be generated.

AI has already proven to be useful for Remaining Useful Life (RUL) predictions, anomaly detection, and classification of failure modes - i also have two such examples that i've published in linkedin with high accuracy. Further applications could include: - clustering to assist with classification - topic modelling on free text fields - apriori and rule association for BOMs and issued components.

I believe AI is beneficial in determining High-level changes in predictive data. Anything that warrants deeper analysis by a qualified and experienced human.

AI enhances predictive maintenance by analyzing data patterns, leveraging machine learning to predict equipment issues, and optimizing schedules, resulting in proactive identification, precise predictions, and efficient maintenance – ultimately reducing downtime and costs.

1. Machine Learning Algorithms: Implementing machine learning models to analyze historical data and predict equipment failures before they occur. 2. Anomaly Detection: Utilizing AI for anomaly detection in sensor data, identifying unusual patterns that may indicate potential issues with machinery. 3. Condition Monitoring Sensors: Integrating AI with sensors to continuously monitor equipment conditions, providing real-time insights into performance and health. 4. Predictive Analytics Platforms: Deploying predictive analytics platforms powered by AI to assess data trends, anticipate maintenance needs, and optimize schedules.

*Vibration Analysis:* AI algorithms can analyse equipment vibrations to detect abnormalities or patterns indicative of potential failures in machinery like motors or turbines. *Sensor Data Monitoring:* Utilising sensors to collect real-time data, AI algorithms can predict equipment failures by identifying deviations from normal operating conditions, enabling timely intervention. *Energy Consumption Patterns:* AI can analyze energy consumption patterns to predict equipment failures, as abnormalities in energy usage may indicate issues with machinery.

Organizations that rush to implement these types of initiatives experience failure at over 300% higher rates than those who take a more calculated and engineered approach. Don't just deploy "sensors" in your facility to check a technology box. Businesses are taking money from you by selling "machine learning, AI, sensor technology" and creating more reactive work for the end customer. You are getting nothing more than a check engine light without proper deployment, analytics, systems, tools, training, and culture.

Is AI a new buzzword? question i ask myself sometimes. Since Industrial Revolution, Industry 1.0 we may label it, was about mechanisation, water and steam powers, Industry 2.0, mass prodution, 3.0 computers, automated prodution, robotics, Industry 4.0 IoT, networking, machine learning, etc. looking at future it is likely going to be human-robot collaboration, AI. It represents a paradigm shift. Practical Applications: AI is being successfully applied to real-world problems, from healthcare diagnostics and autonomous vehicles to predictive maintenance and personalized recommendations, capturing public attention and interest.