Reliability improvement in the presence of weak fault features using non-Gaussian IMF selection and AdaBoost technique

Shifat, Tanvir Alam; Hur, Jang Wook

doi:10.1007/s12206-021-0709-7

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…It is an implementation of boosting algorithm and an efficient and accurate classification algorithm. Since its invention, it has been widely used in various fields [25]. The working mechanism of the Adaboost algorithm is as follows: (a) the training set and a weak learning algorithm are determined, and an initial weight is assigned to each training sample.…”

Section: Design and Improvement Of Feature Fusermentioning

confidence: 99%

Bearing fault diagnosis method based on multi-source heterogeneous information fusion

Zhang¹,

Gao²,

Shi³

2022

Meas. Sci. Technol.

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Bearing fault diagnosis is a critical component of the mechanical equipment monitoring system. In the complex and harsh environment in which bearings operate, the fault diagnosis approach of multi-source information fusion can extract fault features more stably and extensively than the traditional single-source fault diagnosis method. However, most existing multi-source fusion methods are in infancy, and there are a number of pressing issues to address, such as subjective elements having a significant impact, excessive data redundancy, fuzzy multi-source signal fusion strategy, and insufficient accuracy. As a result, a new multi-source fusion fault diagnosis method is proposed in this paper. First, the residual pyramid algorithm is utilized to fuse the acoustic and vibration signals of multiple spatial positions respectively and then form two fused acoustic and vibration signals. Second, two improved 2D-CNN are used to extract the fault features contained in the above two signals separately to form a multi-source fault feature set. Third, an AdaBoost algorithm with a dynamic deletion mechanism is designed to fuse multi-source fault feature sets and produce the fault diagnosis findings. Finally, six different experimental data sets are used to test the performance of the model. The results reveal that the model has better generalization, higher and more stable fault diagnostic accuracy, and stronger anti-interference capacity.

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Section: Design and Improvement Of Feature Fusermentioning

confidence: 99%

Bearing fault diagnosis method based on multi-source heterogeneous information fusion

Zhang¹,

Gao²,

Shi³

2022

Meas. Sci. Technol.

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“…Adaboost's key disadvantage is that it requires a high-quality dataset. Before using an Adaboost algorithm, avoid using noisy data and outliers [53,54]. In a supervised situation, when we are provided with a dataset with target labels, k nearest neighbours (KNN) can be utilized for classification.…”

Section: Overview Of Machine Learning-based Algorithmsmentioning

confidence: 99%

A Feature Engineering-Assisted CM Technology for SMPS Output Aluminium Electrolytic Capacitors (AEC) Considering D-ESR-Q-Z Parameters

Kareem

Hur

2022

Processes

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Recent research has seen an interest in the condition monitoring (CM) approach for aluminium electrolytic capacitors (AEC), which are present in switched-mode power supplies and other power electronics equipment. From various literature reviews conducted and from a failure mode effect analysis (FMEA) standpoint, the most critical and prone to fault component with the highest percentage is mostly capacitors. Due to its long-lasting ability (endurance), CM offers a better paradigm for AEC due to its application. However, owing to severe conditions (over-voltage, mechanical stress, high temperature) that could occur during use, they (capacitors) could be exposed to early breakdown and overall shutdown of the SMPS. This study considered accelerated life testing (electrical stress and long-term frequency testing) for the component due to its endurance in thousands of hours. We have set up the experiment test bench to monitor the critical electrical parameters: dissipation factor (D), equivalent series resistance (ESR), quality factor (Q), and impedance (Z), which would serve as a health indicator (HI) for the evaluation of the AECs. Time-domain features were extracted from the measured data, and the best features were selected using the correlation-based technique.

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“…The (FP) value is the proportion of wrongly classed predictions, while the (FN) value represents the proportion of improperly labelled inputs belonging to a falsely classified class. Since it is the best criterion for implementing and selecting the appropriate model, the model with the lowest (FN) is the best choice [46][47][48][49][50].…”

Section: Supervised Learning Algorithm Evaluation Metricsmentioning

confidence: 99%

Towards Data-Driven Fault Diagnostics Framework for SMPS-AEC Using Supervised Learning Algorithms

Kareem

Hur

2022

Electronics

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The service life of aluminium electrolytic capacitors is becoming a critical design factor in power supplies. Despite rising power density demands, electrolytic capacitors and switching devices are the two most common parts of the power supply that age (deteriorate) under normal and diverse working conditions. This study presents a fault diagnostics framework integrated with long-term frequency for a switched-mode power supply aluminium electrolytic capacitor (SMPS-AEC). Long-term frequency condition monitoring (CM) was achieved using the advanced HIOKI LCR meter at 8 MHz. The data acquired during the experimental study can help to achieve the needed paradigm from various measured characteristics of the SMPS/power converter component to detect anomalies between the capacitors selected for analysis. The CM procedure in this study was bound by the electrical parameters—capacitance (Cs), equivalent series resistance (ESR), dissipation factor (DF), and impedance (Z)—-acting as degradation techniques during physical and chemical changes of the capacitors. Furthermore, the proposed methodology was carried out using statistical feature extraction and filter-based correlation for feature selection, followed by training, testing and validation using the selected supervised learning algorithms. The resulting assessment revealed that with increased data capacity, an improved performance was achieved across the chosen algorithms out of which the k-nearest neighbors (KNN) had the best average accuracy (98.40%) and lowest computational cost (0.31 s) across all the electrical parameters. Further assessment was carried out using the fault visualization aided by principal component analysis (PCA) to validate and decide on the best electrical parameters for the CM technique.

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Reliability improvement in the presence of weak fault features using non-Gaussian IMF selection and AdaBoost technique

Cited by 5 publications

References 35 publications

Bearing fault diagnosis method based on multi-source heterogeneous information fusion

Bearing fault diagnosis method based on multi-source heterogeneous information fusion

A Feature Engineering-Assisted CM Technology for SMPS Output Aluminium Electrolytic Capacitors (AEC) Considering D-ESR-Q-Z Parameters

Towards Data-Driven Fault Diagnostics Framework for SMPS-AEC Using Supervised Learning Algorithms

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