How to Do Predictive Maintenance the Right Way

Predictive maintenance is a technology that has proven to increase the uptime of assets by over 30 percent. This is done through machine learning techniques that identify equipment vulnerabilities and connect them to a maintenance management system. By implementing predictive maintenance, businesses can reduce unexpected failures by over 55 percent.

Predictive maintenance uses data and technology to monitor machinery and identify problems before they happen. It also reduces the cost of repairs and downtime. The result is improved operational efficiency and increased productivity.

Predictive maintenance combines sensors, models, and artificial intelligence to predict when a piece of equipment needs to be repaired or replaced. To achieve the best results, organizations should consult with their equipment manufacturers. This will help them develop the correct maintenance strategy.

The main advantage of predictive maintenance is that it allows facilities to perform maintenance only when needed. For example, in power plants, the company would only want to run an asset once it breaks down, which can cause astronomical costs.

The use of AI and big data has led to predictive maintenance systems that collect and store massive amounts of data. These systems analyze the data in real-time and notify staff when a problem is imminent.

Predictive maintenance is the practice of collecting data and performing analytics on that data to determine whether an asset is likely to fail and then taking action to prevent or delay that failure. Predictive maintenance is often performed as a supplement to traditional preventive maintenance.

Predictive maintenance can significantly improve the overall availability of equipment in an operating plant. It combines condition monitoring techniques and advanced data analysis to make predictions about the lifecycle of assets. This improves the reliability and uptime of equipment, thus minimizing unscheduled downtime.

Predictive maintenance uses real-time or portable instrumentation to recognize symptoms of an impending machine failure. These can include the reduced amplitude of vibration signatures or the presence of special error codes.

Advanced technologies such as machine learning and artificial intelligence have been used to develop effective and efficient maintenance strategies. By leveraging this information, organizations can implement appropriate programs to replace routine maintenance tasks with more effective and proactive alternatives.

The Internet of Things is changing the way maintenance teams manage assets. By connecting machines and devices, the technology allows for automated processes and enhanced performance. But maintaining these devices requires regular updates and periodic maintenance.

To avoid unnecessary breakdowns and emergency repairs, CMMS software with IoT capabilities can be a great asset. In addition to preventing stockout problems, it can increase productivity and streamline maintenance operations.

Maintenance teams need to access accurate data on their machine and infrastructure. When IoT technology is used, the information can be sent to computerized maintenance management systems (CMMS). CMMS software can track inventory and physical assets and analyze real-time readings to improve efficiency.

A CMMS with IoT capability can also help bridge data communication and enable mobile asset monitoring. The combination of these two technologies is a game-changer for any organization. With a CMMS and IoT, companies can streamline routine maintenance activities, reduce costs, and minimize downtime.

Identifying equipment vulnerabilities for predictive maintenance is a necessary process. It improves an asset's performance and lifespan and minimizes the risks of premature equipment failure. Besides, it helps to reduce capital expenses.

Machine learning techniques are used to predict equipment failure before it occurs. They can also be used to identify defects and notify maintenance teams. Depending on the application, there are various machine learning methods to choose from. Deep learning models, XGBoost, support vector machines (SVM), and random forests are among them.

Data preprocessing is often required to clean the data. Once the data is clean, it must be stored and analyzed for meaningful insights. This can be accomplished through resampling. However, this process can take a lot of work.

The first step in algorithm development is to have enough data. More data can lead to useful predictions. Therefore, a robust ML model is necessary.

Some research groups have been working on the topic. These include the Engine Research Group and the Electric Vehicle Battery Research Group.