Fault Detection and Diagnosis in Industrial Systems: A Comprehensive Review
Industrial systems are complex and often involve multiple components and subsystems that operate together to achieve a common goal. These systems are prone to faults and failures, which can lead to significant downtime, costly repairs, and safety risks. Therefore, effective fault detection and diagnosis (FDD) methods are essential to ensure the reliable and safe operation of industrial systems. In this article, we review recent research in FDD methods and their applications in various industrial sectors.
One of the most widely used FDD methods is the Fisher discriminant analysis (FDA). He et al. (2005) proposed a new fault diagnosis method that uses fault directions in FDA. The method has shown promising results in fault detection and classification for various industrial systems, including chemical processes, power plants, and aerospace applications.
Another approach to FDD is data-driven modeling, which involves building mathematical models based on historical data and using them to detect and diagnose faults. Dai and Gao (2013) proposed a data-driven perspective of FDD, which emphasizes the importance of integrating models, signals, and knowledge to improve fault detection and diagnosis accuracy. The authors also discussed various modeling techniques, including multivariate statistical analysis, neural networks, and support vector machines.
Ma and Jiang (2011) reviewed the applications of FDD methods in nuclear power plants. They highlighted the importance of FDD in ensuring the safe and reliable operation of nuclear power plants and discussed various techniques, including statistical process control, neural networks, and fuzzy logic. The authors also emphasized the need for integrating multiple FDD techniques to achieve better fault detection and diagnosis accuracy.
Partial least squares regression (PLSR) is another popular FDD method that has shown promising results in various industrial applications. Ding (2014) discussed the application of PLSR in fault diagnosis and fault-tolerant control systems. The author highlighted the advantages of PLSR, including its ability to handle complex and noisy data and its ability to provide interpretable results.
Recently, deep neural networks (DNNs) have emerged as a promising tool for FDD, particularly for rotating machinery with massive data. Jia et al. (2016) discussed the potential of DNNs in fault characteristic mining and intelligent diagnosis of rotating machinery. The authors highlighted the advantages of DNNs over traditional methods, including their ability to handle high-dimensional data and their ability to learn complex patterns automatically.
In the field of hydraulics, FDD methods have been used to diagnose various faults, including seal wear and internal leakage. Jin et al. (2018) proposed a fault diagnosis method for hydraulic seal wear and internal leakage using wavelets and wavelet neural networks. The authors demonstrated the effectiveness of their method in detecting and diagnosing faults in hydraulic systems.
Similarly, Lin et al. (2014) proposed a new classifying method for mechanical seal condition based on acoustic emission and wavelet neural networks. The authors demonstrated the effectiveness of their method in classifying the condition of mechanical seals, which is critical for ensuring the reliable operation of rotating machinery.
In the nuclear industry, FDD methods are essential for monitoring the condition of critical components, such as pumps and hydraulic cylinders. Gu et al. (2013) proposed an online vibration monitoring system for nuclear reactor main pumps based on PXI. The authors demonstrated the effectiveness of their system in detecting and diagnosing faults in real-time.
Shu et al. (2021) developed a fault diagnosis method for vibration induced by fluid in the main pump of a CPR1000 unit. The authors demonstrated the effectiveness of their method in detecting and diagnosing faults in the main pump, which is critical for ensuring the safe and reliable operation of the nuclear power plant.
Finally, Goharrizi and Sepehri (2013) compared the effectiveness of fast Fourier and wavelet transforms in diagnosing actuator leakage. The authors demonstrated that wavelet transforms are more effective in detecting and diagnosing actuator leakage, which is critical for ensuring the reliable operation of hydraulic systems.
In conclusion, FDD methods are essential for ensuring the reliable and safe operation of industrial systems. Recent research has focused on developing new FDD methods and improving the accuracy and effectiveness of existing methods. The applications of FDD methods in various industrial sectors, including chemical processes, power plants, and hydraulics, have demonstrated their effectiveness in detecting and diagnosing faults. However, there is still a need for further research to develop more robust and accurate FDD methods for complex industrial systems.
- Wavelet analysis
- Input data length determination
- Operation trend diagnosis
- Data analysis
- Time series analysis
News Source : SpringerLink
Source Link :Determination of the Inputting Data Length for the Diagnosis of the Operation Trend Based on Wavelet Analysis/