The roller bearing fault diagnosis methods with harmonic wavelet packet and multi-classification relevance vector machine

Roller bearings are widely used elements in rotary machines. How to monitor the working conditions of roller bearings are focus study in the world. Monitoring the vibration signals of roller bearings is important indirect mean for that they reveal the characteristics and feature of roller bearing faults. Therefore, monitor the vibration signals and diagnose the working states of roller bearings are widely used to ensure the safety operation of the machines. This paper studies a novel roller bearing faults discrimination method with harmonic wavelet packet and Multi-classification Relevance Vector Machine (MRVM). Indeed, the fault discrimination is a pattern recognition process including feature extraction and faulty patterns recognition. Therefore, this paper collects vibration signals and decomposes them with harmonic wavelet packet. After the wavelet coefficients of each node are available, compute the vector energy by corresponding coefficients. The feature vector is prepared after the vector energy has been standardized. With MRVM, the paper proposes three fault discrimination methods in order to identify good bearing, bearing with inner race fault, bearing with outer race fault and bearing with roller fault. The Decision Tree (DT) model, One Against Rest (OAR) model and One Against One (OAO) model are used to propose the classification methods respectively. The proposed OAO model is simplified in order to improve the computation efficiency and simplify the architecture of the model. Finally, capture the vibration signal from the roller bearing stand of electric engineering lab and the roller bearing fault simulation stand QPZZ-II to illustrate the proposed methods. The proposed feature extraction method with harmonic wavelet packet is compared with conventional wavelet packet. With the previous feature vectors, the accuracy and efficiency of the three fault discrimination methods are compared. The accuracy and efficiency of three fault discrimination methods are compared under different conditions including developing faults, noise involving and several faults developing simultaneously. Experiment results show that the proposed feature extraction method is more effective than conventional method and the simplified OAO-RVM model possess the best fault discrimination accuracy and DT-RVM model possesses the better computation efficiency

Generative Adversarial Networks Selection Approach for Extremely Imbalanced Fault Diagnosis of Reciprocating Machinery

At present, countless approaches to fault diagnosis in reciprocating machines have been proposed, all considering that the available machinery dataset is in equal proportions for all conditions. However, when the application is closer to reality, the problem of data imbalance is increasingly evident. In this paper, we propose a method for the creation of diagnoses that consider an extreme imbalance in the available data. Our approach first processes the vibration signals of the machine using a wavelet packet transform-based feature-extraction stage. Then, improved generative models are obtained with a dissimilarity-based model selection to artificially balance the dataset. Finally, a Random Forest classifier is created to address the diagnostic task. This methodology provides a considerable improvement with 99% of data imbalance over other approaches reported in the literature, showing performance similar to that obtained with a balanced set of data.National Natural Science Foundation of China, under Grant 51605406National Natural Science Foundation of China under Grant 7180104

Comparison of feature extraction from wavelet packet based on reconstructed signals versus wavelet packet coefficients for fault diagnosis of rotating machinery

Vibration signals from rotating machines are usually nonlinear and nonstationary. Time frequency techniques are suitable for analyzing this type of signals. Wavelet analysis is one of the most powerful methods in this regards. Therefore, wavelet analysis is used extensively for diagnosis of nonlinear and nonstationary signals. Faults in rotating machines show their effects in certain frequency bands. In this research the features extracted from reconstructed signals from wavelet packets were compared to features extracted from wavelet packet coefficients. It is shown that reconstructed signals act better for fault diagnosis than wavelet packet coefficients. To support our claim one example is designed that justifies our hypothesis. To evaluate our hypothesis in real world practical situations, health condition monitoring of a motorcycle gearbox has been considered. In this practical situation wavelet coefficients and reconstructed signals from wavelet packet coefficients extracted from signals acquired from gearbox housing were compared. Mahalanobis distance (MD) is employed to evaluate the significance of the extracted features. It is shown that features extracted from reconstructed signals are more suitable than features extracted from wavelet packet coefficients

Observer-biased bearing condition monitoring: from fault detection to multi-fault classification

Bearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems. (C) 2016 Elsevier Ltd. All rights reserved.Grant number: 145602

Diagnosis of Combination Faults in a Planetary Gearbox using a Modulation Signal Bispectrum based Sideband Estimator

This paper presents a novel method for diagnosing combination faults in planetary gearboxes. Vibration signals measured on the gearbox housing exhibit complicated characteristics because of multiple modulations of concurrent excitation sources, signal paths and noise. To separate these modulations accurately, a modulation signal bispectrum based sideband estimator (MSB-SE) developed recently is used to achieve a sparse representation for the complicated signal contents, which allows effective enhancement of various sidebands for accurate diagnostic information. Applying the proposed method to diagnose an industrial planetary gearbox which coexists both bearing faults and gear faults shows that the different severities of the faults can be separated reliably under different load conditions, confirming the superior performance of this MSB-SE based diagnosis scheme

Online milling tool condition monitoring with a single continuous hidden Markov models approach

The health status evolving from normal to broken condition of a milling tool is needed as an object of assessment in condition-based maintenance. This paper proposes continuous hidden Markov models (CHMM) to assess the status of the tool online based on the normal dataset in the same case. A wavelet-packet decomposition technology is used to feature extraction and the CHMM is trained by Baum-Welch algorithm. Finally, we compute the log-likelihood based on the trained CHMM for abnormal detection and health assessment in real time during the milling process. A case study on tool state estimation demonstrates the effectiveness and potential of this methodology