A deep condition feature learning approach for rotating machinery based on MMSDE and optimized SAEs

Self Cite

Rotating machinery is a primary element of mechanical equipment, and thus fault diagnosis of its key components is very important to improve the reliability and safety of modern industrial systems. The key point to diagnose the faults of these components is to extract effectively the hidden fault information. However, the actual vibration signals of rotating machinery have nonlinear and non-stationary characteristics, so traditional signal decomposition methods are unable to extract the frequency components accurately, leading to spectrum overlap of the decomposed sub-signals. Therefore, a rotating machinery fault diagnosis approach based on Fourier transform multi-filter decomposition (FTMFD), fuzzy entropy (FE), joint mutual information maximization (JMIM), and a light gradient boosting machine (LightGBM), is proposed in this paper. FTMFD is used to extract the frequency domain information of the raw vibration signals, whereas FE is used to calculate and extract the fault information of the decomposed sub-signals. Then feature selection is carried out by using JMIM to reduce the influence of redundant features on data analysis and classification accuracy. Furthermore, LightGBM is used to rank the candidate features and outputs the fault diagnosis result. Experimental results from two real datasets show that the proposed method achieves higher accuracy with fewer features than some existing methods for fault recognition. Various working conditions are also considered and verified.

“…Firstly, a dataset of mixed rotor and bearing faults from the Machinery Fault Simulator (MFS) platform was used to verify the effectiveness of the proposed approach [48,49].…”

Section: Data Description and Experimental Setupmentioning

confidence: 99%

Fault diagnosis of key components in the rotating machinery based on Fourier transform multi-filter decomposition and optimized LightGBM

Zhang

Kong

et al. 2020

Self Cite

“…MTF images contain temporal correlations and features at different time scales can be extracted through multidimensional feature extraction. Ge et al [26] proposed an improved multiscale symbolic dynamic entropy, which extraction of multiscale fault signature information by calculating the entropy of the original vibration signal. Although these methods have demonstrated improved robustness in fault diagnosis, the multiscale model also incorporates unnecessary information into the learning domain, and the unnecessary information will affect the final discriminative classification when it enters the deep structure of the network.…”

Section: Introductionmentioning

confidence: 99%

Small sample fault diagnosis for wind turbine gearbox based on lightweight multiscale convolutional neural network

Wang,

Tong

2023

The maintenance and diagnosis of wind turbine gearboxes are crucial for enhancing the stability and operational efficiency of wind power systems. However, there are still two challenges in gearbox fault diagnosis methods based on deep learning : (1) Limited failure sample; (2) Interference of strong noise. To solve the above issues, a lightweight multiscale convolutional neural network (LMSCNN) based fault diagnosis method is proposed in this paper. Among them, a large kernel convolution is used to denoise the original vibration signal. A lightweight multiscale architecture is constructed using depthwise separable convolutional blocks (DSCBs), which mine fault features at different scales and improve the operational efficiency of the model. Moreover, a parallel global pooling block (PGPB) is designed to provide a more comprehensive feature for the fusion layer, enabling the effective diagnosis of vibration signals. Experiments are conducted on the datasets of two different gearboxes, which prove that LMSCNN has excellent generalization capability and diagnostic speed.

“…A variational auto-encoder was introduced to realize data amplification by vibration signal generation [24]. Bayesian optimization-based SAEs are applied to select feature samples and classify the fault status in mechanical fault diagnosis [25]. A convolutional adversarial AE was proposed to recognize unseen faults [26].…”

Section: Introductionmentioning

confidence: 99%

An intelligent fault diagnosis method for an electromechanical actuator based on sparse feature and long short-term network

Yang¹,

Guo²,

Zhao³

2021

Electromechanical actuators (EMAs), as the new generation of actuators, have an important impact on the safety of aircraft. With the development of measurement technology, a large amount of data provides a broad prospect for the data-based fault diagnosis method. However, the existence of redundant data increases the burden of software and hardware. Therefore, a semi-supervised sparse auto-encoder (SSAE) is employed to prune observed data based on sparsity analysis. Moreover, temporal and spatial relationships are explored by a multi-channel long short-term network to build a time series model, so as to perform fault detection and isolation based on the difference between its estimated and observed values. Due to its sparse feature extraction capability, the SSAE can improve the fault isolation accuracy while pruning observed data. Verification results confirm that the proposed method can effectively diagnose EMA faults.