Predictive maintenance is the monitoring of equipment using data to help predict and ultimately prevent equipment failures. In other words, it is identifying the patterns in the data that indicate changes in equipment condition. The main goal of predictive maintenance is to prevent unplanned maintenance of equipment. Predictive maintenance uses sensor data and other equipment related data to monitor the performance of equipment in real-time.
IoT and data types:
One of the key elements of predictive maintenance is the Internet of Things(IoT). IoT allows for connecting of equipment to make them work together, share and analyze the data. Some of the common data types that come out of sensors are heat, pressure, vibration, and sound, etc. Apart from the sensor data, the other data types such as equipment usage data and maintenance data, and metadata about the equipment is also used in predictive maintenance
Once the data set is gathered, choosing the correct machine learning model is important to achieve the goals of predictive maintenance. Before machine learning is used on data, data exploration is done, where-in the data scientists build confidence in the data set to contain patterns.
Machine learning frameworks for common cases of predictive maintenance:
There are four common cases in predictive maintenance. They are 1) predicting the equipment failure 2) finding out the remaining life of the equipment 3) predicting the anomaly in equipment behavior 4) optimizing the equipment setting to maximize its performance
Equipment failure: The goal is to predict whether the equipment will fail in a given period or not. This is referred to as a classification problem. The remaining life of the equipment: The goal is to predict the remaining life or value of the equipment. This is referred to as a regression problem.
Anomaly in equipment behavior: The focus is on identifying abnormal, unusual, or irregular patterns, as they contrast to how you expect equipment to work in a typical fashion. This is referred to as a pattern-matching problem.
Optimize equipment settings: Use machine learning to automatically determines ideal behavior within a specific context to maximize performance. This is referred to as an optimization problem.
Advantages of predictive maintenance:
The advantages of predictive maintenance are many. But the major ones are 1) it decreases the cost of equipment maintenance 2) it decreases the equipment breakdowns and downtime 3) it significantly improves the overall efficiency and throughput of the equipment.
Surveys have shown that predictive maintenance has led to a 70% decrease in asset breakdown, a 30% decrease in asset downtime and a 25% decrease in asset maintenance; means major cost savings and significant improvement in efficiency.