How to build the equipment failure model of PHM？

　　Predictive maintenance and health management (PHM) is a key concept in modern industry. It aims to realize the early prediction and prevention of equipment failure by using data and advanced analysis technology, so as to maximize the availability and reliability of equipment. In the process of realizing PHM, the construction of equipment fault model is a crucial step, which provides the basis for predictive maintenance algorithm. This paper will discuss the construction process of PHM's equipment fault model and its application in predictive maintenance.

　　Figure. Predictive Maintenance of Equipment (iStock)

　　1. Data collection and processing

　　Before building the equipment fault model, a large amount of equipment data needs to be collected and processed first. With the help of the digital platform of the equipment, various sensor data, operation status and performance parameters of the equipment can be obtained in real time by connecting with the equipment. These data include but are not limited to temperature, pressure, vibration, current, voltage, etc. Data processing is a key step, and the accuracy and consistency of data are ensured through data cleaning, denoising and normalization.

　　Figure. Installing sensors on equipment to collect data (PreMaint)

　　2. Feature extraction and selection

　　Extracting effective features from original data is a key step in building equipment fault model. Features are indicators or parameters used to describe the state and performance of equipment, and they are the basis for predicting equipment failures. The equipment digitization platform uses advanced feature extraction algorithm to automatically extract and select the most representative features from the original data. These characteristics may include frequency domain characteristics, time domain characteristics, statistical characteristics and so on.

　　3. Fault marking and data marking

　　A large number of fault samples need to be trained to build the equipment fault model, so it is necessary to mark and label the data. The equipment digitization platform can automatically mark the data and classify the data in normal state and fault state through the information such as equipment fault alarm and maintenance records. In addition, the equipment digitization platform also supports users to manually mark data to further improve the accuracy of the model.

　　4. Establish a fault model

　　After the data preparation and labeling are completed, the next step is to establish the equipment fault model. The equipment digital platform provides a variety of modeling algorithms, including machine learning algorithm and deep learning algorithm, which can be selected according to the characteristics and requirements of data. The training and optimization of the model is an iterative process. By constantly adjusting the parameters and structure of the model, the prediction accuracy and stability of the model are improved.

　　5. Fault prediction and maintenance decision

　　When the equipment failure model is established, it can be used to predict the equipment failure. The equipment digitization platform monitors the equipment status and performance in real time, and uses the fault model to predict the fault. Once it is predicted that the equipment is about to break down, the platform will send an alarm to notify the maintenance personnel in time so that they can take corresponding maintenance measures. Maintaining the accuracy and timeliness of decision-making will significantly improve the availability and production efficiency of equipment.

　　Figure. Fault alarm (PreMaint)

　　Through the above steps, we can see how the equipment digital platform can help enterprises build equipment fault models and apply them to predictive maintenance. This PHM method based on data and advanced algorithm enables enterprises to find signs of equipment failure earlier and take corresponding maintenance measures, thus minimizing equipment downtime and maintenance costs and improving equipment reliability and availability. Digital equipment platform provides powerful tools and support for enterprises to achieve efficient predictive maintenance, and helps enterprises to move towards a more intelligent and sustainable future.