Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network

Zhang, Zhenyou; Wang, Yi; Wang, Kesheng

doi:10.1007/s10845-012-0657-2

Cited by 218 publications

(104 citation statements)

References 42 publications

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“…The process of applying any of the techniques or methodologies to monitor, detect and diagnose faults in electric motors has stages that are common to all, and the differences are due to the particularities of each technique. The generic steps for a process of detecting and diagnosing failures in electric induction motors include sensing, acquiring, filtering, processing and monitoring the signal for detection and diagnosis (see Figure 6) [64,65]. The equipment used in each of these phases is shown in Table 11.…”

Section: Software and Hardware Used For Monitoring Detection And DImentioning

confidence: 99%

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

2017

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Despite the complex mathematical models and physical phenomena on which it is based, the simplicity of its construction, its affordability, the versatility of its applications and the relative ease of its control have made the electric induction motor an essential element in a considerable number of processes at the industrial and domestic levels, in which it converts electrical energy into mechanical energy. The importance of this type of machine for the continuity of operation, mainly in industry, is such that, in addition to being an important part of the study programs of careers related to this branch of electrical engineering, a large number of investigations into monitoring, detecting and quickly diagnosing its incipient faults due to a variety of factors have been conducted. This bibliographic research aims to analyze the conceptual aspects of the first discoveries that served as the basis for the invention of the induction motor, ranging from the development of the Fourier series, the Fourier transform mathematical formula in its different forms and the measurement, treatment and analysis of signals to techniques based on artificial intelligence and soft computing. This research also includes topics of interest such as fault types and their classification according to the engine, software and hardware parts used and modern approaches or maintenance strategies.

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Section: Software and Hardware Used For Monitoring Detection And DImentioning

confidence: 99%

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

2017

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“…It uses a combination of short-time Fourier transform-based SK, kurtogram, adaptive SK, and protrugram [42]. However, despite such specific situations, the wavelet transform has been the most popular denoising technique for the extraction of the defect vibratory signature from the measured signal in which the random noise and other parameters of the bearing are immersed [43][44][45]. With respect to data processing, the stochastic process inherent to bearing wear can be classified as a single component which depends on the nature of the degradation state: discrete or continuous [46].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves

Pino

Ribas

Guimarães

2018

Shock and Vibration

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The contribution of a medium-sized hydro power plant to the power grid can be either at base load or at peak load. When the latter is the most common operation mode, it increases the start and stop frequency, intensifying the hydro turbine components' degradation, such as the guide bearings. This happens due to more frequent operation in transient states, which means being outside the service point of the machines' nominal condition, consisting of speed, flow, and gross head. Such transient state operation increases the runner bearings' mechanical vibration. The readings are acquired during the runner start-ups and filtered by a DC component mean value and a wavelet packet transform. The filtered series are used to estimate the relationship between the maximum orbit curve displacement and the accumulated operating hours. The estimated equation associated with the ISO 7919-5 vibration standards establishes the sojourn times of the degradation states, sufficient to obtain the transition probability distribution. Thereafter, a triangular probability function is used to determine the observation probability distribution in each state. Both matrices are inputs required by a hidden Markov model aiming to simulate the equipment deterioration process, given a sequence of maximum orbit curve displacements.

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“…The output of ANN is RUL prediction or performance degradation assessment, which is used for conducting effective maintenance strategies. ANNs widely used in fault prediction include BPNN [91][92][93][94][95], radial basis function network (RBFN), and RNN [96]. Ahmadzadeh, et al [94], proposed a three-layer feedforward BPNN for RUL estimation of grinding mill liners, which considered degeneration and condition monitoring data as the inputs of ANN, and used RUL as the output of ANN.…”

Section: Annmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network

Cited by 218 publications

References 42 publications

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

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