One thing can be said with certainty that condition monitoring has become an integral part of industrial equipment maintenance game since it helps detect potential failures before they disrupt operation. If we look at the past, this tedious process relies heavily on manual inspections and the expertise of dedicated maintenance personnel. But we all know that the probability of human error is always there. And with the integration of machine learning, this field has been transformed to the extent that industries are opting for proactive maintenance models instead of reactive ones.

So, let’s dig deep into finding out everything you need to know about machine learning’s role in improving conditioning monitoring.

The Evolution of Condition Monitoring with Machine Learning                          

One of the many reasons why machine learning has become the industry-disrupting tool is that it offers a robust framework for analyzing data collected through various sensors and IoT devices installed on machinery. These important devices play a role of monitoring a wide range of parameters such as temperature, vibration, and pressure. Doing so, these devices help provide a continuous steam of data that machine learning uses to create baseline metrics for normal operations. But if there are any deviations, machine learning will help identify that there are problems like malfunctions or wear and tear.

Some of the core applications of machine learning in condition monitoring are:

Predictive Maintenance: One could say that the cornerstone of machine learnig’s impact on condition monitoring is predictive maintenance. After all, ML models predict when and where equipment might fail by using historical and real-time data. This allows maintenance to be scheduled at the most opportune time to prevent issues down the road.

Anomaly Detection: Other reason ML algorithms are so effective is that they help identify those data pattern that somewhat deviate from the norm. For instance, anomaly detection systems monitor equipment data to flag unusual readings like for temperature, vibration or anything else. This certainly helps safeguard the equipment and ensure a safe working environment.

Root Cause Analysis: We all know that failure can occur at any given time. And what that failure finally happens, ML comes into play by analyzing data to determine the underlying causes. This helps a lot in understanding why the failure happened and what could be done to prevent it in the future. This also increases the safety and the lifespan of the equipment.

Digital Twins and Operational Optimization: Did you know that machine learning can also help create digital twins – virtual models of physical assets? It is quite fascinating how machine learning models can simulate the real-world behavior of their physical counterparts under various scenarios. This in return can play a critical role in detailed analysis and testing without any risk of damaging actual equipment. When used correctly, digital twins can be a powerful tool for optimizing the operation and maintenance of industrial assets.

Implementing Machine Learning in Condition Monitoring               

While there are many benefits of machine learning, the challenge is how you integrate it into condition monitoring. For instance, the quality and consistency of date used for training ML models should be better. If the collected data is poor, it can lead to inaccurate predictions and misguided maintenance efforts.

Moreover, ML models are complex and that why it requires expertise of specialized companies like Artesis to properly implement them in condition monitoring. If implemented by an expert team such as the one at Artesis, machine learning and conditioning monitoring can offer unprecedented capabilities to predict, detect, and analyze equipment failures.

We can imagine you have many questions regarding how to implement machine learning into conditioning monitoring or like how conditioning monitoring can help businesses like yours. Please feel free to contact us anytime and we’d be more than happy to assist you in making a calculated decision regarding the betterment of your company.