Study on Noise Elimination of Mechanical Vibration Signal Based on Improved Wavelet

Yuan, Wei

doi:10.1109/icmtma50254.2020.00039

Cited by 6 publications

(12 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Raw vibrational and pressure sensor data are non-stationary in nature, contain background noise, and usually provide little information for direct use for most prognostics problems, including the one presented in this work [17]. This has essentially prompted the need for several signal processing techniques for reliable feature extraction.…”

Section: A Signal De-noisingmentioning

confidence: 99%

“…This has essentially prompted the need for several signal processing techniques for reliable feature extraction. Particularly, choosing a safe threshold for signal de-noising depends on the targeted system's dynamics, engineer/analyst's level of expertise, and/or familiarity in the domain; nevertheless, by separating useful signals from background noise, a more reliable health assessment for accurate prognostics can be achieved [17], [18].…”

Section: A Signal De-noisingmentioning

confidence: 99%

“…The wavelet transform represents a signal in the form of wavelet series-a representation of a square-integrable (real or complex-valued) function by a certain orthogonal series generated by a wavelet [17], [18]. Similar to discrtete Fourier transform (DFT) and short-time Fourier transform (STFT), wavelet transform can be viewed as the projection of a signal into a set of basis functions called wavelets.…”

Section: A Dwt De-noising/decompositionmentioning

confidence: 99%

See 2 more Smart Citations

An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

Akpudo

Hur

2021

IEEE Access

View full text Add to dashboard Cite

The remaining useful life (RUL) prediction of industrial cyber-physical system components demands the use of reliable prognostics parameters and frameworks. Against the traditional use of a single measure of degradation, data from multiple sensors provide abundant characteristic information for modeling, assessing, and extracting useful parameters via appropriate signal processing and sensor fusion methods. This study introduces a multi-sensor prognostics approach which merges highly prognosible statistical features from vibrational and pressure sensor measurements after a multi-level wavelet decomposition of the signals. The prognostic algorithm presented in this work for solenoid pump RUL prediction is a multi-objective genetic algorithm-optimized long short-term memory (MOGA-LSTM) which accepts the fused sensor features as input and returns the RUL of the pump as output. The framework was tested on a run-to-failure experiment on a VSC63A5 Solenoid pump following a significant pump malfunction caused by a clogged suction filter after the test. Using standard prognostic performance evaluation metrics, the performance of the prognostics framework was compared with other reliable state-of-the-art methods with a remarkable comparative advantage in addition to better automation potentials for real-time condition monitoring and RUL prediction.

show abstract

Section: A Signal De-noisingmentioning

confidence: 99%

Section: A Signal De-noisingmentioning

confidence: 99%

Section: A Dwt De-noising/decompositionmentioning

confidence: 99%

See 1 more Smart Citation

An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

Akpudo

Hur

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Also, Continuous wavelet transform (CWT) is popular for nonlinear characterization of nonstationary and transient signals [28]. This versatile technique which employs wavelet function(s) in place of sinusoidal functions enhances a scale variable in addition to the time variable in the inner product transformation and is popularly used for signal demodulation and band-pass filtering [29], [30]. For fault diagnosis, CWT coefficients provide a wide array of nonlinear properties for fault isolation/classification and these have shown strong effectiveness for planetary gearbox fault detection [31], water pump impeller damage detection [32], bearing fault detection [33], etc.…”

Section: Motivation and Related Work A Vibration Monitoring Andmentioning

confidence: 99%

“…As a result of the wavelet basis function of the WT, wavelet decomposition of a non-stationary signal into linear forms of time-scale units is possible, thereby reconstructing the signal into several components according to the wavelet function translation [29], [30]; however, as fault features, wavelet coefficients are quite efficient [32], [33].…”

Section: ) Continuous Wavelet Transformmentioning

confidence: 99%

A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features

Akpudo

Hur

2020

IEEE Access

View full text Add to dashboard Cite

Accurate condition monitoring of industrial cyber-physical systems/components demands the use of reliable fault detection and isolation (FD&I) methodologies. Meta-heuristic algorithms for feature selection have good exploration capability for optimal discriminative feature selection for fault isolation/classification of which the Binary Particle swarm optimization (BPSO) is superior to its counterparts. This study presents a robust approach for vibration-based failure diagnostics of electromagnetic/solenoid pumps which employ a multi-domain feature extraction procedure (statistical time-domain and frequencydomain features, Mel frequency cepstral coefficients, and continuous wavelet coefficients) for capturing linear and nonlinear properties from the signals. Compared with other filter and wrapper methods for supervised feature selection, a hybrid filter-wrapper (Pearson's correlation-BPSO (ρ-BPSO)) feature selection procedure is proposed for global search of optimal discriminative (uncorrelated) features for fault diagnosis with an RBF-kernel support vector machine (SVM*). Subsequently, a practical case study involving five VSC63A5 solenoid pumps at various operating/fault conditions is presented for validating the performance of the proposed approach. Results show the superior performance of the proposed hybrid filterwrapper approach against filter-based and wrapper-based techniques for discriminative feature selection. Also, the proposed ρ-BPSO-SVM* diagnostics model performance was compared with other standard fault isolation/classification methods.

show abstract

Fault Diagnosis Method for Industrial Robots Based on DBN Joint Information Fusion Technology

Jiao¹,

Zheng²

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Aiming at the problems of the traditional industrial robot fault diagnosis model, such as low accuracy, low efficiency, poor stability, and real-time performance in multi-fault state diagnosis, a fault diagnosis method based on DBN joint information fusion technology is proposed. By studying the information processing method and the deep learning theory, this paper takes the fault of the joint bearing of the industrial robot as the research object. It adopts the technique of combining the deep belief network (DBN) and wavelet energy entropy, and the fault diagnosis of industrial robot is studied. The wavelet transform is used to denoise, decompose, and reconstruct the vibration signal of the joint bearing of the industrial robot. The normalized eigenvector of the reconstructed energy entropy is established, and the normalized eigenvector is used as the input of the DBN. The improved D-S evidence theory is used to solve the problem of fusion of high conflict evidence to improve the fault model’s recognition accuracy. Finally, the feasibility of the model is verified by collecting the fault sample data and creating the category sample label. The experiment shows that the fault diagnosis method designed can complete the fault diagnosis of industrial robot well, and the accuracy of the test set is 97.96%. Compared with the traditional fault diagnosis model, the method is improved obviously, and the stability of the model is good; the utility model has the advantages of short time and high diagnosis efficiency and is suitable for the diagnosis work under the condition of coexisting multiple faults. The reliability of this method in the fault diagnosis of the joint bearing of industrial robot is verified.

show abstract

Study on Noise Elimination of Mechanical Vibration Signal Based on Improved Wavelet

Cited by 6 publications

References 1 publication

An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features

Fault Diagnosis Method for Industrial Robots Based on DBN Joint Information Fusion Technology

Contact Info

Product

Resources

About