Unexpected asset downtime can be a significant operational risk for companies. A sudden failure of a primary machine or a system can disrupt operations, resulting in lost productivity, revenue, and delayed client fulfillment. It can also result in additional costs, such as replacement or repair costs, employee overtime compensation, and fines for failing to meet the contractual requirements.
Businesses can reduce this risk by implementing machine learning-powered preventative maintenance programs, which can help companies maximize equipment availability while reducing unscheduled downtime. In this blog post, we’ll explore the role of machine learning in predictive maintenance, how it works, and how businesses can optimally benefit from it.
How machine learning helps in predictive maintenance
One of the major challenges in predictive maintenance is detecting irregularities in machine behavior that could lead to failure. Traditional maintenance methods rely on regular inspections, which can be time-consuming and costly. Machine learning algorithms can help automate this process by examining data in real-time and alerting maintenance teams when issues must be looked into. Here’s how it works –
A machine learning model is built. The model is trained using historical data, including data on machine failures. Once the model is trained, it is deployed in a production environment. Now, sensors are embedded in machinery and equipment to collect data in real time. ML models analyze this data, and alerts are sent to maintenance teams when they detect anomalies requiring attention.
How businesses are using machine learning in predictive maintenance
GE Aviation uses AI and machine learning algorithms to predict when its jet engines are likely to fail. The algorithms can detect changes in the performance of specific components and anticipate when these components may fail by evaluating data from sensors placed in engines. This method has enabled GE Aviation to reduce unscheduled engine removal from civil airliners by over 33%.
Here are a few examples of how firms are utilizing machine learning in predictive maintenance:
Failure prediction: Machine learning models can evaluate historical data from sensors and other sources to discover patterns and abnormalities that lead to equipment breakdowns. Businesses can plan maintenance activities in advance to avoid unplanned downtime by forecasting when a malfunction is likely to occur.
Root cause analysis: Machine learning models can also assist in determining the root cause of equipment failures by examining data from several sources and associating various elements that lead to the failure. This can help firms in addressing the underlying causes and prevent future failures.
Prescriptive maintenance: In addition to anticipating breakdowns, machine learning models can prescribe the best maintenance measures to perform, depending on the current state of the equipment and other contextual data. This can assist organizations in optimizing maintenance procedures, lowering expenses, and extending the life of their equipment.
Asset health monitoring: Machine learning models can continuously monitor the health and performance of equipment, identifying any degradation or anomalies that require attention. Businesses may make informed judgments about maintenance tasks and optimize equipment use by offering real-time analytics.
Condition-based maintenance: Rather than depending on a predetermined schedule for maintenance activities, machine learning models can examine real-time data from sensors and other sources to determine the best time for maintenance tasks based on a variety of factors.
How can businesses optimally benefit from machine learning in predictive maintenance
Here are some ways that businesses can optimally benefit from machine learning in predictive maintenance:
Data collection: Gathering and organizing data from various sources is necessary for training machine learning models. Businesses can develop a comprehensive view of the equipment’s health, performance, and consumption trends by integrating data from sensors, IoT devices, maintenance logs, and other sources.
Feature engineering: It is the process of selecting and transforming relevant data attributes to improve the performance of machine learning models. Domain knowledge can be used by businesses to detect critical factors like temperature, vibration, pressure, or humidity and engineer them to extract additional information from raw data.
Model selection: For predictive maintenance, different machine learning methods such as Random Forests, Gradient Boosting, and Neural Networks can be utilized. Businesses can compare many models and choose the one that works best for their use case.
Continuous learning: Machine learning models must be updated with new data regularly to enhance their accuracy and efficacy over time. Businesses can use feedback loops to learn from maintenance operations, identify new failure modes, and refine existing models.
Predictive maintenance has been an area of great interest for industries across the globe. The ability to predict when a machine is likely to fail and hence schedule maintenance can save billions of dollars in maintenance costs and minimize the downtime of critical machinery. As machine learning algorithms continue to evolve, the potential for predictive maintenance will only grow, and we can expect to see more organizations adopting this technology to improve their operations.
If you want to learn more about leveraging machine learning for predictive maintenance, email us at email@example.com. Intellect Data, Inc. is a software solutions company incorporating data science and AI into modern digital products. IntellectDataTM develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellectdata.com.