Gearbox Running Research Articles

Artificial Neural Network-Based Fault Diagnosis of Gearbox using Empirical Mode Decomposition from Vibration Response

This paper presents a gearbox defect diagnosis based on vibration behaviour. In order to record the vibration response under various circumstances, an industrial gearbox was used as the basis for an experimental setup. The signals resulting from gear wear were processed using an empirical mode decomposition for two operating time intervals (zero-hour running time and thirty-hour running time). The first three intrinsic mode functions and the corresponding frequency response were detected. The ten statistical parameters most sensitive to gear wear were selected using an evaluation method based on Euclidean distance. Using the identified features, an artificial neural network (ANN) was trained to track the gearbox for the selected future data set. The neural network received its input from the statistical parameters, and its output was the number of gearbox running hours. To achieve faster convergence, the radial basis function and the backpropagation neural network were compared. The superiority of the proposed strategy is demonstrated by comparing the performance of ANN. For monitoring the condition of industrial gears, the proposed strategy is found to be effective and trustworthy.

Combining the theoretical bound and deep adversarial network for machinery open-set diagnosis transfer

Recently, deep transfer learning-based intelligent machine diagnosis has been well investigated, and the source and the target domain are commonly assumed to share the same fault categories, which can be called as the closed-set diagnosis transfer (CSDT). However, this assumption is hard to cover real engineering scenarios because some unknown new fault may occur unexpectedly due to the uncertainty and complexity of machinery components, which is called as the open-set diagnosis transfer (OSDT). To solve this challenging but more realistic problem, a Theory-guided Progressive Transfer Learning Network (TPTLN) is proposed in this paper. First, the upper bound of transfer learning model under open-set setting is thoroughly analyzed, which provides a theoretical insight to guide the model optimization. Second, a two-stage module is designed to carry out distracting unknown target samples and attracting known samples through progressive learning, which could effectively promote inter-class separability and intra-class compactness. The performance of proposed TPTLN is evaluated in two OSDT cases, where the diagnosis knowledge is transferred across bearings and gearbox running under different working conditions. Comparative results show that the proposed method achieves better robustness and diagnostic performance under different degrees of domain shift and openness variance.The source codes and links to the data can be found in the following GitHub repository: https://github.com/phoenixdyf/Theory-guided-Progressive-Transfer-LearningNetwork.

A multi-fault diagnosis method of gear-box running on edge equipment

Edge computing equipment is a new tool that has been widely used to monitor the operation state of industrial equipment and to diagnose and analyze faults. Therefore, the fault diagnosis algorithm used in the edge computing device plays an especially significant role in fault diagnosis. The application of deep learning method in mechanical fault diagnosis has been gradually popularized, because it has many advantages, such as strong classification ability and accurate feature extraction ability. However, many of the completed papers and models are based on single label system and are used to diagnose single target fault. The validation set is not rigorous enough, and it is difficult to accurately simulate the faults that may occur in the actual production process. Nowadays, in the era of big data, the single label system ignores the joint relationship of different fault types, and it is difficult to make a correct judgment for the location, type and degree of mechanical failure. Hence, in the process of experiment, we used the bearing data of Case Western Reserve University(CWRU) to ensure the wide range and large quantity of data sets. A fault diagnosis method of gear and bearing in the gear-box based on multi-task deep learning model is put forward. In this method, gear and bearing faults can be diagnosed simultaneously. Through a separate task layer, this method can adaptively extract the characteristics of distinct targets from the same signal, and add a Batch Normalization layer(BN) to accelerate the convergence speed of the network. Through experiments, we conclude that it is an effective method which can judge the fault situation of gear and bearing accurately in a variety of working conditions.

Applications of Ceemdan in Dynamic Behavior of Defected Spur Gearbox Running Under Acyclism Regime

ABSTRACTEmpirical Mode Decomposition (EMD) and its approaches are powerful techniques in signal processing especially for the diagnosis of rotating machinery running in non-stationary regime. We are interested in this paper to the dynamic behavior of a defected one stage gearbox equipped with an elastic coupling and loaded under acyclism regime generated by a combustion engine. Actually, we adopt an approach to the EMD method called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) as a technique to perform the diagnosis of the studied system. Since the obtained signals are modulated, all obtained Intrinsic Mode Functions (IMFs) are modulated and are processed and shown by the Wigner-Ville distributions (WVD) as well as the spectrum of their envelope in order to detect defects such as cracked tooth defect in the wheel of the spur gearbox and eccentricity defect in the gear.

Gear fault diagnosis under non-stationary operating mode based on EMD, TKEO, and Shock Detector

Condition monitoring of gearboxes running under non-stationary operating conditions is a very difficult task. In this study, a signal processing technique is developed for damage detection of a bevel gearbox running under variable load and speed conditions. The proposed technique is applied on simulated vibration data computed through a dynamic model of bevel gearbox. The procedure used in this technique is based on the extraction of the shock related to the defect using the Shock Detector (SD) method. Firstly, vibration signals are decomposed into IMFs using Empirical Mode Decomposition (EMD). Then, the Teager–Kaiser Energy Operator (TKEO) is used to assess the instantaneous energy of the signal. Afterwards, SD is applied to examine and quantify the shock contents of the TKEO signal, which reflect the effect of the defect.

Fault diagnosis of wind turbine gearbox based on wavelet neural network

In order to monitor the wind turbine gearbox running state effectively, a fault diagnosis method of wind turbine gearbox is put forward based on wavelet neural network. Taking a 1.5 MW wind turbine gearbox as the target of study, the frequency spectrum of vibration signal and the fault mechanism of driving part are analyzed, and the eigenvalues of the frequency domain are extracted. A wavelet neural network model for fault diagnosis of wind turbine gearbox is established, and wavelet neural network is trained by using different feature vectors of fault types. The relationship between fault component and vibration signal is identified, and the vibration fault of wind turbine gearbox is predicted and diagnosed by network model. The analysis results show that the method can diagnose fault and fault pattern recognition of wind turbine gearbox very well.

Relevance of Correlation Dimension and Kolmogorov Entropy in the State Discriminant of Gearbox

This paper introduces the basic principles and calculation methods for the correlation dimension and Kolmogorov entropy. By calculating the correlation dimension and Kolmogorov entropy when the gear is under different working conditions, we can analyze the inherent relationship between the two in depicting of the running condition of the gearbox. The result shows that,the correlation dimension and Kolmogorov entropy have a good consistency in the description of working status of gearbox. This conclusion not only provides a good basis for the gearbox running condition judgment and fault diagnosing, but can also provide the experimental basis for the chaotic characteristic parameters selection in state monitoring and fault diagnosing.

A gearbox fault diagnosis scheme based on near-field acoustic holography and spatial distribution features of sound field

Vibration signal analysis is the main technique in machine condition monitoring or fault diagnosis, whereas in some cases vibration-based diagnosis is restrained because of its contact measurement. Acoustic-based diagnosis (ABD) with non-contact measurement has received little attention, although sound field may contain abundant information related to fault pattern. A new scheme of ABD for gearbox based on near-field acoustic holography (NAH) and spatial distribution features of sound field is presented in this paper. It focuses on applying distribution information of sound field to gearbox fault diagnosis. A two-stage industrial helical gearbox is experimentally studied in a semi-anechoic chamber and a lab workshop, respectively. Firstly, multi-class faults (mild pitting, moderate pitting, severe pitting and tooth breakage) are simulated, respectively. Secondly, sound fields and corresponding acoustic images in different gearbox running conditions are obtained by fast Fourier transform (FFT) based NAH. Thirdly, by introducing texture analysis to fault diagnosis, spatial distribution features are extracted from acoustic images for capturing fault patterns underlying the sound field. Finally, the features are fed into multi-class support vector machine for fault pattern identification. The feasibility and effectiveness of our proposed scheme is demonstrated on the good experimental results and the comparison with traditional ABD method. Even with strong noise interference, spatial distribution features of sound field can reliably reveal the fault patterns of gearbox, and thus the satisfactory accuracy can be obtained. The combination of histogram features and gray level gradient co-occurrence matrix features is suggested for good diagnosis accuracy and low time cost.

Gearbox incipient fault diagnosis using feature sample selection and principal component analysis

Monitoring and diagnostic systems have played an important role in modern industry. Many intelligent or signal processing methods have been successfully applied in the manufacturing process. Inspired by the research of kernel function approximation (KFA), a novel kernel principal component analysis (KPCA) method is proposed in this paper and applied in gearbox incipient fault detection. The proposed KPCA is realised by feature sample selection and principal component analysis, which can also be called FSS-PCA. The key issues studied in this paper are non-linear feature extraction, optimal feature sample selection and diagnostic performance assessment. Firstly, the integral operator Gaussian kernel function is used to realise the non-linear map from the raw input space of gearbox vibration features to a high dimensional space, where appropriate feature samples are selected to construct the feature subspace. Then PCA is used to classify two kinds of gearbox running conditions: normal and tooth crack. The quantity of selected samples is much fewer than that of whole sample sets, which has quickly expedited the computation process. Experiment results indicate the effectiveness of FSS-PCA for gearbox incipient fault diagnosis.