System failure is common across all use cases of machines and equipment. But for a certain context, it is crucial to predict the failure in time and, accordingly, take preventive measures to avoid such failure. 

Fortunately, we now have machine learning technology at our disposal to make precise predictions of system failure that can easily be prevented. By assessing the working of certain parts and equipment within a machine, maintenance work can be scheduled, and the entire system can be prevented from breaking down. 

Such predictive maintenance can prevent car breakdowns, aviation accidents, and failure of mission-critical systems exposing people and resources to risks.   

While periodic maintenance based on the gross assessment and life cycles of parts is still the most common approach to prevent sudden failure, it cannot guarantee that a system will not break down before reaching the threshold time for periodic maintenance. 

As most users consider uncompromising system performance as the most important value proposition of tech gadgets and devices, predictive maintenance is gaining more importance than ever before. 

Here, through the rest of the blog post, we will explain how machine learning (ML) technology empowers predictive maintenance and the key ML techniques used by predictive maintenance. Let’s start. 

Finding the Ideal Machine Learning Techniques for Predictive Maintenance

Source: Maruti Techlabs

Predictive maintenance by using Machine Learning tries to learn from historical data and use live data to detect the patterns of system failure. In contrast to traditional maintenance procedures relying on the life cycle of machine parts, the ML-based predictive approach prevents loss of resources and under-optimized utilization of resources for maintenance tasks. 

Predicting failure at the right time ML technology helps mitigate the fault lines in time while not draining resources. This ensures establishing the right balance between maintenance needs and resource utilization. 

Utilizing data to the optimum level  

Source: Intuceo

For years, machine learning technology has made significant contributions in delivering the most precise predictions for different industry scenarios and contexts. Thanks to Machine Learning technology, making precise and highly reliable forecasts and utilizing those forecasts for perfectly timed maintenance tasks has become easier than ever before. 

Preventive maintenance in certain industries is part of the standard maintenance protocols. For example, in railways and aviation, preventive maintenance based on data-driven analytics has been in use for years. In these industries, the signals from the attached sensors are tracked to evaluate system health and accordingly provide support. In addition, such sensor data organized and analyzed through an analytics engine provides a historical analysis of the typical failure, system issues, and timing. 

While these data-driven insights and test results of machines help to implement the same maintenance drives for multiple systems in an environment, they also help establish a standard maintenance timeline besides helping to reduce resource utilization and cost.

Moving from idea to reality 

Source: The Data Scientist

To understand how machine learning can make value additions to the predictive maintenance protocols, it is important to understand its journey from merely a great concept to an era-defining reality for the industries. The most significant impetus for ML-based predictive maintenance is the exposure to the massive volume of digital data. 

From the huge volume of system data, an ML program can easily draw the most relevant insights to assess the system issues. Moreover, as many leading organizations, including top banks, have already used ML, the technology is already time-tested for its efficiency. 

On the other hand, just having a large volume of data is not enough. This data needs to be utilized for training the machine learning model with sharp attention on how the device and equipment interactions are changing because of the newly trained ML models. Furthermore, to make sure that the data facilitates precise learning for highly predictable output from the ML model, data needs to be cleaned and structured. Only then, the ML model can ensure optimum accuracy in guiding the system behaviour. 

Machine Learning Techniques for Predictive Maintenance

Source: Tryolabs

Over the years, some predictive maintenance techniques based on machine learning stood out as most effective. For predictive maintenance, one needs to attach sensors to the respective system, and the sensor will track and gather data concerning the system operations. This data fetched and gathered by sensors for predictive maintenance appears with time series comprising timestamp and sensor readings. Such timestamped data can help the ML-based application to predict the failure accurately with precise timing.

Machine learning-based predictive maintenance is mainly created by using either of the two techniques mentioned below. 

• Classification approach: This predictive approach makes predictions of the possibility of failure in any of the upcoming steps.  

• Regression approach: This approach makes prediction of the time left before a system is failed. This predicted time before system failure is also called as Remaining Useful Life (RUL).

While the first approach comes with a gross answer, it can achieve more precision while using very little data. On the other hand, the latter uses more data, but it also delivers more details about the impending failure. Industries widely use these ML-based predictive approaches to assess system failure and prevent them in advance. 

As the last piece of advice, we request you to prepare yourself with the following steps to make the most of a predictive maintenance journey. 

• First of all, come with a proper definition and clarification of the use case. 

• Ake uses existing data or produces a new set of data that goes well for the respective use case. 

• When fetching data for your use case, utilize available data exploration techniques. 

• Make an assessment of whether the data needs to incorporate failure patterns. 

• When you can see clear evidence of failure patterns, the same can be used for developing Machine Learning models.

Conclusion 

Let us make it clear once and for all that creating a machine learning model for predictive maintenance cannot follow a one-size-fits-all approach. The strategy to fetch data and process the data for the machine learning model will entirely depend on the maintenance tasks and specific challenges in dealing with the system. Not all system failures can be predicted through one model, and not all ML models can be created by following one strategy.