# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 1 Introduction  
With the continuous improvement of the industrialization level, the design and manufacturing technologies of the equipment have also advanced swiftly, making the equipment's safety and reliability increasingly significant [1]. Equipment in operation is constantly exposed to various environmental forces (such as cutting forces, friction, ambient temperature, and vibration), and is susceptible to wear and tear, rusting of parts, deterioration of components, and other problems, leading to frequent abnormalities in the equipment, resulting in a gradual decline in its efficiency and life, and even catastrophic failures. To address these issues, condition monitoring of equipment has attracted tremendous research attention to ensure the safety and reliability of production [2; 3; 4; 5; 6; 7]. Based on online monitoring of time-series data, the failure process can be separated into two stages for most of the equipment, as depicted in Figure 1. The first phase is the normal operating phase, which begins with the installation and commissioning of the system and ends with the appearance of abnormalities. During this phase, the monitored data is relatively steady and has no discernible pattern of change. The second stage is the progression from the appearance of anomalies to the degradation and eventual failure of the equipment. If the abnormalities discovered in this phase lead directly to equipment failure, a fault detection and diagnostic model is constructed based on the monitored data to determine the fault type, severity, and location. If the data observed during this phase reveals a tendency of degradation that is not severe enough to impede the normal operation of the equipment, a model can be constructed to predict the failure and estimate the remaining useful life of the equipment based on the degradation trend. There are three steps in equipment condition monitoring: (1). data collection; (2). data processing; and (3). Fault detection, diagnosis, and prediction. Data collection refers to the real-time monitoring of parameters or signals relevant to the performance state of the equipment using various types of sensors, and the aggregation of the collected data. Data processing refers to the pre-processing of signals or data obtained by sensors, including noise reduction, reconstruction, and feature extraction. Features include time-domain features, frequency-domain features, time-frequency-domain features, and features autonomously learnt by deep learning models. Lastly, for the detected deviations from normal operation, intelligent fault diagnosis algorithms are used to determine the specific fault type of the equipment, and for equipment exhibiting signs of degradation or failure, prediction algorithms are used to predict the future trend of the equipment's performance state and its remaining useful life. In general, the method to condition monitoring has undergone three stages of development, with the first relying on manual monitoring and diagnosis by specialized engineers. The second development stage produces the data-driven condition monitoring based on the continuous growth of sensor technology, more and more novel sensors are installed or integrated in the equipment to gather real-time monitoring information of the equipment, and a large number of signal analysis techniques are suggested and implemented in the monitoring sector, realizing the contemporary fault diagnosis technology with fault mechanism, sensor monitoring, and signal analysis as the co-main technologies. The most renowned of these are mathematics and statistics-based techniques, including Fourier Transform (FT), Wavelet Transform (WT), and Empirical Modal Decomposition (EMD) [8; 9; 10]. The third stage of development is artificial intelligence-based condition monitoring, where artificial intelligence techniques, mainly represented by machine learning, have been quickly developed.