# Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique
## 1 Introduction  
Industrial equipment is usually complicated and features multiple failure modes. As a large-scale rotate machine, once failure occurs, a long-time repairing, even shutdown, is required, resulting in a significant economic loss. On the other hand, industrial field characterized harsh operational environment, equipment would experience a high failure rate, therefore, accurate condition monitoring for industrial equipment is necessary, and plays a pivotal role in condition-based maintenance, which would be more beneficial than corrective and preventive maintenance [1, 2]. [PERSON] [3] concluded the general condition monitoring for industrial equipment, including temperature, chemical emission, vibration, electrical current, and electrical discharging. Furthermore, the paper illustrated the statistics distribution of failure modes in industrial equipment in the past decades, according to the research, rotation related failure occupies nearly a half (44%), higher than stator related (35%) and bearing related failure (21%), and according to previous work, unexpected vibration often occurs before serious failure events [4, 5]. As a typical condition monitoring parameter, vibration is chosen for example and investigated in our paper. The aim of condition monitoring is inferring the condition of equipment rapidly, and giving a clear indication for incipient failure modes using minimum measurements. In industrial fields, vibrations are directly measured, for example, vibrations data are recorded in supervisory information system (SIS) in thermal power plants. In normal operating conditions, the vibrations of different directions are controlled in a certain range. The unexpected increase of vibrations corresponds to over-load, poor lubrication, bearing loosening, rotor unbalance, or shaft bending [6]. Therefore,effective condition monitoring could evaluate equipment effectively, and the failures would be detected in early stage. The state-of-the-art for rotate equipment vibration condition monitoring is concluded into directly signal monitoring and model-based monitoring. In the aspect signal analysis, time domain analysis and frequency domain analysis are effective methods for condition monitoring of rotate component vibration [6; 7; 8; 9]. However, for large rotating equipment, such as blower fan, there are some difficult to realize signal condition monitoring: (1) blower fan working at variable power load and frequency. Working condition of rotating equipment changes frequently, which leads to frequency band width of vibration changed, resulting in poor accuracy of failure location. (2) The industrial environment is complicated, the vibration sources of each component are multiple and seriously coupling, therefore, the vibration analysis of components is difficult to reflect characteristic of blower fan. With the development and application of information technology in industrial fields, operating data, including vibration data, of equipment is recorded automatically in detail, which makes it possible to achieve condition monitoring using data-driven analysis methods. Expert system is an effective implementation for data-driven artificial intelligent method, an expert system for industrial machine vibration analysis is designed for condition monitoring, and the knowledge set was commonly constructed using operating data and laboratory and industry validation [10; 11]. Neural network (NN) is an available method for nonlinear vibration; the model between recorded parameters and vibration state is established; the knowledge in historical data is acquired in the course of model training. The derivative neural network, such as recurrent neural network and convolutional neural network are also applied in vibration condition monitoring [12; 13; 14; 15; 16]. However, the weaknesses of NN are also obvious, the training process is indispensable and it is always time-consuming.