Variational mode decomposition: mode determination method for rotating machinery diagnosis

Variational mode decomposition (VMD) is a modern decomposition method used for many engineering monitoring and diagnosis recently, which replaced traditional empirical mode decomposition (EMD) method. However, the performance of VMD method specifically depends on the parameter that need to pre-determine for VMD method especially the mode number. This paper proposed a mode determination method using signal difference average (SDA) to determine the mode number for the VMD method by taking the advantages of similarities concept between sum of variational mode functions (VMFs) and the input signals. Online high-speed gear and bearing fault data were used to validate the performance of the proposed method. The diagnosis result using frequency spectrum has been compared with traditional EMD method and the proposed method has been proved to be able to provide an accurate number of mode for the VMD method effectively for rotating machinery applications

A novel hybrid extreme learning machine-whale optimization algorithm for bearing fault diagnosis

In order to avoid fatalities and ensure safe operation, a good and accurate diagnosis method is required. A diagnosis method based on extreme learning machine (ELM) has attracted much attention and the ELM method had been applied in various field of study. The advantages of the ELM method which are rapid learning rate, better generalization performance and ease of implementation makes the ELM method suitable to be used in various field including fault diagnosis fields. However, the performance of the ELM method becomes inefficient due to incorrect selection of neurons number and randomness of input weight and hidden layer bias. Hence, this paper aims to propose a novel hybrid fault diagnosis method based on ELM and whale optimization algorithm (WOA), known as ELM-WOA for bearing fault diagnosis. Four different types of bearing datasets from Case Western Reserve University Bearing Data Centre were used in this paper in order to present the performance of the proposed method. Based on the result, the performance of the proposed method was able to surpass the performance of the conventional ELM

A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis

Signal processing method is very important in most diagnosis approach for rotating machinery due to non-linearity, non-stationary and noise signals. Recently, a new adaptive signal decomposition method has been proposed by Dragomiretskiy and Zosso known as variational mode decomposition (VMD). The VMD method has merit in solving mode mixing problem in most conventional signal decomposition method. This paper aims to review the applications of the VMD method in rotating machinery diagnosis. The advantages and limitations of the VMD method are discussed. Current solution on VMD limitation also have been review and discussed. Lastly, the future research suggestion has been pointed out in order to enhance the performance of the VMD method on rotating machinery diagnosis

A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis

Signal processing method is very important in most diagnosis approach for rotating machinery due to non-linearity, non-stationary and noise signals. Recently, a new adaptive signal decomposition method has been proposed by Dragomiretskiy and Zosso known as variational mode decomposition (VMD). The VMD method has merit in solving mode mixing problem in most conventional signal decomposition method. This paper aims to review the applications of the VMD method in rotating machinery diagnosis. The advantages and limitations of the VMD method are discussed. Current solution on VMD limitation also have been review and discussed. Lastly, the future research suggestion has been pointed out in order to enhance the performance of the VMD method on rotating machinery diagnosis

A novel blade fault diagnosis using a deep learning model based on image and statistical analysis

Artificial intelligence technology has a high potential for machinery fault detection and diagnosis. Blade component failure is the main type of failure that usually occur in gas turbine and this component tends to fail unexpectedly. Detection and diagnosis of blade components are different with gear and bearing as both components have a standard vibration analysis and the fault can be examined using frequency domain analysis. Due to the complex structure of the blade system, the informative feature from the vibration signal on the blade fault often obscure with the noise signal. Therefore, this paper proposed a system using a combination of time–frequency image analysis and a stacked sparse autoencoder (SSAE) model to tackle the challenge of blade fault detection and diagnosis. The experiment is carried out using a multi-stage blade system and the result showed that proposed system is able to provide more than 90% diagnosis performance

Optimized ELM based on Whale Optimization Algorithm for gearbox diagnosis

Extreme learning machine (ELM) is a fast and quick learning algorithm with better generalization performance. However, the randomness of input weight and hidden layer bias may affect the overall performance of ELM. This paper proposed a new approach to determine the optimized values of input weight and hidden layer bias for ELM using whale optimization algorithm (WOA), which we call WOA-ELM. An online gearbox vibration signals is used in this study. Empirical mode decomposition (EMD) and complementary mode decomposition (CEEMD) are used to decompose the signals into sub-signals known as intrinsic mode functions (IMFs). Then, statistical features are extracted from selected IMFs. WOA-ELM is used for classification of healthy and faulty condition of gearbox. The result shows that WOA-ELM provide better classification result as compared with conventional ELM. Therefore, this study provide a new diagnosis approach for gearbox application

Optimized ELM based on Whale Optimization Algorithm for gearbox diagnosis

Extreme learning machine (ELM) is a fast and quick learning algorithm with better generalization performance. However, the randomness of input weight and hidden layer bias may affect the overall performance of ELM. This paper proposed a new approach to determine the optimized values of input weight and hidden layer bias for ELM using whale optimization algorithm (WOA), which we call WOA-ELM. An online gearbox vibration signals is used in this study. Empirical mode decomposition (EMD) and complementary mode decomposition (CEEMD) are used to decompose the signals into sub-signals known as intrinsic mode functions (IMFs). Then, statistical features are extracted from selected IMFs. WOA-ELM is used for classification of healthy and faulty condition of gearbox. The result shows that WOA-ELM provide better classification result as compared with conventional ELM. Therefore, this study provide a new diagnosis approach for gearbox application

Empirical mode decomposition: A review on mode selection method for rotating machinery diagnosis

Rotating machinery diagnosis is very important to ensure safe operation and avoid fatalities. Failure of rotating machinery causes production loss, equipment loss and financial loss. Therefore, condition monitoring and fault diagnosis are very crucial to most critical engineering application such as helicopter, aircraft and gas turbine. Empirical mode decomposition (EMD) method is a well-known method that has been widely and successfully used for rotating machinery diagnosis over decades. Selection of IMF is very important to have most information extracted from vibration signal and avoiding misled interpretation. This review paper aims to review and summarise the selection method used to select most significance IMF for rotating machinery diagnosis. Besides that, this review paper also has proposed some fruitful research direction for future consideration