Analysis of weak faults of planetary gears based on frequency domain information exchange method

This paper focuses on solving a series of problems, in particular, the extraction of planetary gear fault characteristics for cracked and broken teeth, using the frequency domain information exchange method. First, we discuss deficiencies in classical stochastic resonance fault feature extraction method. A number of issues are associated with adaptive stochastic resonance based on the re-scaling frequency method used during the small parameter issues, such as sampling frequency ratio constraints and easily induced aliasing of the target frequency band. Second, to overcome the above-mentioned problems, this paper proposes a frequency domain information exchange optimization method. Simulations were carried out used the proposed method and results were compared to those obtained using previously presented adaptive stochastic resonance based on the re-scaling frequency method. Finally, tests were performed on an experimental planetary gearbox failure platform to further verify the frequency domain information exchange method for effectively extracting planetary gear crack and missing tooth fault features

PHM survey: implementation of signal processing methods for monitoring bearings and gearboxes

The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes

Fault diagnosis of motorized spindle via modified empirical wavelet transform-kernel PCA and optimized support vector machine

The fault diagnosis of motorized spindle contributes to the improvement of the reliability of computer numerical control machine tools. Presently, numerous mechanical fault diagnosis technologies suffer from the drawbacks of mode mixing, non-adaptive analysis, and low efficiency. Therefore, adopting an effective signal processing method for fault diagnosis of motorized spindle is essential. A method based on modified empirical wavelet transform (EWT) and kernel principal component analysis (Kernel PCA) is proposed. A new method, which determines the proper number of the Fourier spectrum segments, is applied when using EWT. To improve computational efficiency, Kernel PCA is adopted to reduce dimension. The support vector machine optimized by genetic algorithm is introduced to accomplish fault identification. The performance of the proposed method is validated through single and compound fault experiments. Results show that the recognition rate using the proposed method reached 98.8095 % and 98.4375 % in terms of single and compound fault diagnoses, respectively. Moreover, compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), local mean decomposition (LMD) and EWT, the proposed method can save much computing time. The proposed method can be generalized to other mechanical fault diagnoses as well

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Fault Diagnosis of Rotating Machinery using Improved Entropy Measures

Fault diagnosis of rotating machinery is of considerable significance to ensure high reliability and safety in industrial machinery. The key to fault diagnosis consists in detecting potential incipient fault presence, recognizing fault patterns, and identifying degrees of failures in machinery. The process of data-driven fault diagnosis method often requires extracting useful feature representations from measurements to make diagnostic decision-making. Entropy measures, as suitable non-linear complexity indicators, estimate dynamic changes in measurements directly, which are challenging to be quantified by conventional statistical indicators. Compared to single-scale entropy measures, multiple-scale entropy measures have been increasingly applied to time series complexity analysis by quantifying entropy values over a range of temporal scales. However, there exist a number of challenges in traditional multiple-scale entropy measures in analyzing bearing signals for bearing fault detection. Specifically, a large majority of multiple-scale entropy methods neglect high�frequency information in bearing vibration signal analysis. Moreover, the data length of transformed multiple signals is greatly reduced as scale factor increases, which can introduce incoherence and bias in entropy values. Lastly, non-linear and non-stationary behaviors of vibration signals due to interference and noise may reduce the diagnostic performance of traditional entropy methods in bearing health identification, especially in complex industrial settings. This dissertation proposes a novel multiple-scale entropy measure, named Adaptive Multiscale Weighted Permutation Entropy (AMWPE), for extracting fault features associated with complexity change in bearing vibration analysis. A new scale-extraction mechanism - adaptive Fine-to-Coarse (F2C) procedure - is presented to generate multiple-scale time series from the original signal. It has advantages of extracting low- and high-frequency information from measurements and generating improved multiple-scale time series with a hierarchical structure. Numerical evaluation is carried out to study the performance of the AMWPE measure in analyzing the complexity change of synthetic signals. Results demonstrated that the AMWPE algorithm could provide high consistency and stable entropy values in entropy estimation. It also presents high robustness against noise in analyzing noisy bearing signals in comparison with traditional entropy methods. Additionally, a new bearing diagnosis method is put forth, where the AMWPE method is applied for entropy analysis and a multi-class support vector machine classifier is used for identifying bearing fault patterns, respectively. Three experimental case studies are carried out to investigate the effectiveness of the proposed diagnosis method for bearing diagnosis. Comparative studies are presented to compare the diagnostic performance of the proposed entropy method and traditional entropy methods in terms of computational time of entropy estimation, feature representation, and diagnosis accuracy rate. Further, noisy bearing signals with different signal-to-noise ratios are analyzed using various entropy measures to study their robustness against noise in bearing diagnosis. Additionally, the developed adaptive F2C procedure can be extended to a variety of entropy algorithms based on improved single-scale entropy method used in entropy estimation. In the combination of artificial intelligence techniques, the improved entropy algorithms are expected to apply to machine health conditions and intelligent fault diagnosis in complex industrial machinery. Besides, they are suitable to evaluate the complexity and irregularity of other non-stationary signals measured from non-linear systems, such as acoustic emission signals and physiological signals

Customized Multiwavelets for Planetary Gearbox Fault Detection Based on Vibration Sensor Signals

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