Machine learning (ML) has revolutionized many business processes, such as risk management, disease prevention, and stock market trading. In the manufacturing industry, ML is taking predictive maintenance to a whole new level.
This post tackles predictive maintenance using ML, how it differs from other types of maintenance processes, and how predictive maintenance models are implemented.
What Is Predictive Maintenance?
Predictive maintenance is a data-driven method to predict when a piece of equipment may fail so it can be fixed before it breaks down. Take your car, for example. Instead of changing its oil every 3,000 miles, predictive maintenance allows you to monitor how much oil is left. You then get alerted when it reaches the level where it needs a change, which could be before the car reaches the prescribed 3,000 miles.
Predictive maintenance uses ML algorithms and advanced analytics to continuously monitor real-time usage data from sensors. It also takes into account historical maintenance data. By doing so, predictive maintenance models can detect deteriorating machine conditions, find irregularities, and predict potential malfunctions.
With predictive maintenance, related tasks can get scheduled proactively, and only the necessary ones are performed.
Predictive versus Preventive versus Reactive Maintenance
The three approaches to maintenance differ a lot from one another. Predictive maintenance aims to service equipment just before they are about to break, basing decisions on real-time usage data and predictive analytics.
On the other hand, preventive maintenance is based on a regular, prearranged schedule. Even when there are no issues with a machine, preventive maintenance still proceeds as planned, requiring resources and potentially causing operational downtime. As you can see, predictive maintenance can waste labor, materials, and time when it is not truly needed.
Meanwhile, reactive maintenance adapts the “fix it when it breaks” approach. This maintenance type can be costly, as it leads to unexpected downtime and emergency repairs. The equipment may even get damaged all the more by waiting for them to break down before doing something.
How Does Predictive Maintenance Using Machine Learning Work?
Predictive maintenance using ML works by leveraging data and advanced algorithms to predict equipment failures before they occur. It involves data collection and preprocessing, model training and validation, prediction, and maintenance planning. We’ll explain these stages in greater detail below.
Data Collection and Preprocessing
The first step in using ML for predictive maintenance is collecting data from sources, such as historical maintenance records and sensors installed on the equipment. These sensors typically gather real-time information on parameters, such as:
- Acoustic emissions
- Electric current
- Pressure
- Temperature
- Vibration
The data collected is then processed and converted into a suitable format for ML algorithms.
Predictive Maintenance Model Selection
Selecting the right ML model is crucial for the success of a predictive maintenance system. The choice depends on several factors. For one, the type, volume, and quality of data collected influence the model suitable for the system.
Processing power and memory may also limit the choice in predictive maintenance models. In addition, the prediction goal should also be considered. For example, organizations may want to use the system to detect anomalies and suspicious sensor data. In this case, unsupervised learning models will be more appropriate, where the algorithm uses unlabeled data and discovers patterns by itself.
Model Training and Validation
After collecting data and choosing a predictive maintenance model, the algorithm is trained on the dataset. At this stage, the algorithm learns patterns, structures, and relationships between the input data (i.e., historical data and real-time sensor readings) and the output (e.g., equipment health status).
For example, the algorithm would analyze the ideal temperature of the machine based on past data and current sensor readings. Any deviation is flagged as an anomaly. A predefined anomaly or likelihood threshold is also taught to the model.
The trained model also needs to be evaluated to ensure it is accurate. Fine-tuning the parameters may also be part of the model validation process.
Prediction Generation
Once the model is trained, the system can start generating predictions based on real-time data from the sensors. Depending on the predictive maintenance system’s goal, it can:
- Anticipate how much longer the machine is expected to operate before it fails
- Predict how likely the machine will malfunction within a specific timeframe
- Detect unusual patterns that may affect normal operations
Maintenance Planning
When the generated prediction exceeds the predefined threshold, it triggers alerts for scheduling relevant maintenance tasks. This helps organizations efficiently allocate resources, including personnel, tools, and spare parts.
The Importance of Using Machine Learning for Predictive Maintenance
Predictive maintenance using ML minimizes unexpected equipment downtime by predicting and preventing potential failures. Organizations can also allocate resources more efficiently since ML-powered predictive maintenance systems can identify urgent maintenance needs. As a result, unnecessary maintenance tasks are prevented, resulting in lower maintenance costs.
ML models can identify potential catastrophic failures in advance, helping organizations keep their personnel safe and prevent costly accidents.
Moreover, early detection and repair of equipment issues based on real-time usage help extend the lifespan of equipment.
What Are the Challenges in Implementing Predictive Maintenance?
Organizations seeking to implement predictive maintenance using machine learning face significant challenges. One of the major ones is ensuring the data collected is of high quality. Inaccurate or incomplete data can severely affect the system’s reliability.
Aside from that, storing and processing huge amounts of data from multiple sources can be resource-intensive. For example, integrating sensors into existing equipment can be tedious and time-consuming.
