Coupled neurons with multi-objective optimization benefit incipient fault identification of machinery

Dynamic observers are commonly used in feedback loops to estimate the system’s states from available control inputs and measured outputs. The presence of measurement noise degrades the performance of the observer and consequently degrades the performance of the controlled system. This paper presents a novel nonlinear higher-order extended state observer (NHOESO) for efficient state and disturbance estimation in presence of measurement noise for nonlinear single-input–single-output systems. The proposed nonlinear function allows a fast reconstruction of the system’s states and is robust against uncertainties and measurement noise. An analytical parameterization technique is proposed to parameterize the coefficients of the proposed nonlinear higher-order extended state observer in the case of measurement noise in the output signal. Several scenarios are simulated to demonstrate the effectiveness of the proposed observer.

show abstract

“…T . The desired transient profile vector (r 1 , r 2 ) T is obtained using the conventional TD given as in [1],…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…These differences may also degrade the performance of the controlled system. However, in some nonlinear systems the noise is beneficial for enhancing weak signals of interest and signal estimation such as [1].…”

Section: Introductionmentioning

confidence: 99%

Parameterization of a Novel Nonlinear Estimator for Uncertain SISO Systems with Noise Scenario

Azar

Abdulmajeed²,

Majdi³

et al. 2022

Mathematics

View full text Add to dashboard Cite

show abstract

“…[8,9]. Quite significantly, the uproar of DL methods across most disciplines has disparaged the use of traditional model-based methods for fault detection and isolation (FDI) and this can be attributed to the former's superior learning abilities, better automation capabilities, improved predictive capabilities, and automated feature learning efficiencies [10][11][12]. On the down side, the inherent issues of interpretability, high dependence on excessive parameters, overfitting/underfitting, dependence on big data, need for high computational power, and feature evaluation complexity often pose considerable concerns for cost-aware applications [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

FMECA and MFCC-Based Early Wear Detection in Gear Pumps in Cost-Aware Monitoring Systems

2021

View full text Add to dashboard Cite

Gear pump failures in industrial settings are common due to their exposure to uneven high-pressure outputs within short time periods of machine operation and uncertainty. Improving the field and line clam are considered as the solutions for these failures, yet they are quite insufficient for optimal reliability. This research, therefore, suggests a method for early wear detection in gear pumps following an extensive failure modes, effects, and criticality analysis (FMECA) of an AP3.5/100 external gear pump manufactured by BESCO. To replicate this condition, fine particles of iron oxide (Fe2O3) were mixed with the experimental fluid, and the resulting vibration data were collected, processed, and exploited for wear detection. The intelligent wear detection process was explored using various machine learning algorithms following a mel-frequency cepstral coefficient (MFCC)-based discriminative feature extraction process. Among these algorithms, extensive performance evaluation reveals that the random forest classifier returned the highest test accuracy of 95.17%, while the k-nearest neighbour was the most cost efficient following cross validations. This study is expected to contribute to improved evaluations of gear pump failure diagnosis and prognostics.

show abstract

“…As a result, it is difficult to extract useful information from vibration signals and inaccurate results probably obtained with ineffective methods. To solve this problem, Qiao et al [8][9][10][11] applied stochastic resonance to fault diagnosis, making use of the enhancement of noise to periodicity signals generated due to the faults. Multistable stochastic resonance [12] can also be found for fault detection.…”

Section: Introductionmentioning

confidence: 99%

Repetitive Transient Extraction Using the Optimized SES Entropy Wavelet for Fault Diagnosis of Rotating Machinery

Wang

Zhu

et al. 2021

Shock and Vibration

View full text Add to dashboard Cite

Repetitive transients are usually generated in the monitoring data when a fault occurs on the machinery. As a result, many methods such as kurtogram and optimized Morlet wavelet and kurtosis method are proposed to extract the repetitive transients for fault diagnosis. However, one shortcoming of these methods is that they are constructed based on the index of kurtosis and are sensitive to the impulsive noise, leading to failure in accurately diagnosing the fault of the machinery operating under harsh environment. To address this issue, an optimized SES entropy wavelet method is proposed. In the proposed method, the optimized parameters including bandwidth and central frequency of Morlet wavelets are selected. Then, based on the wavelet coefficients decomposed using the optimized Morlet wavelet, the SES entropy is calculated to select the scales of wavelet coefficients. Finally, the repetitive transients are reconstructed based on the denoising wavelet coefficients of the selected scales. One simulation case and vibration data collected from the experimental setup are used to verify the effectiveness of the proposed method. The simulated and experimental analyses showed that the signal-to-noise ratio (SNR) of the proposed method has the largest value. Specifically, the SNR in the experimental analysis of the proposed method is 0.6, while that of the other three methods is 0.043, 0.0065, and 0.0045, respectively. Therefore, the result shows that the proposed method is superior to the traditional methods for repetitive transient extraction from the vibration data suffered from impulsive noise.

show abstract

Coupled neurons with multi-objective optimization benefit incipient fault identification of machinery

Cited by 76 publications

References 35 publications

Parameterization of a Novel Nonlinear Estimator for Uncertain SISO Systems with Noise Scenario

Parameterization of a Novel Nonlinear Estimator for Uncertain SISO Systems with Noise Scenario

FMECA and MFCC-Based Early Wear Detection in Gear Pumps in Cost-Aware Monitoring Systems

Repetitive Transient Extraction Using the Optimized SES Entropy Wavelet for Fault Diagnosis of Rotating Machinery

Contact Info

Product

Resources

About