Development of NN-Based Ball Bearing Fault Diagnosis Techniques

Sharma, Anju; Priya, Taruv Harshita; Patra, G. K.; Naidu, Vps

doi:10.1007/978-981-19-8094-7_5

Cited by 2 publications

(1 citation statement)

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“…Time domain features, time derivatives, and time integration of the time domain features were considered as the features for the fault classification using ANN. Here, all the features were used for classification [7]. Time domain features from acoustic signals are obtained with different speeds, loads, and defect sizes.…”

Section: Introductionmentioning

confidence: 99%

Optimizing bearing health condition monitoring: exploring correlation feature selection algorithm

Sharma,

Priya,

Naidu

2024

Eng. Res. Express

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Vibration signals are a critical source of information for detecting and diagnosing bearing faults, making this research particularly relevant to the field of condition monitoring of industrial machinery particularly bearings using vibration signal. This study delves into how feature selection can be done using Pearson’s Correlation Co-efficient within the context of monitoring bearing health conditions, utilizing two distinct approaches. Approach-1 involves feature selection without considering labels, while Approach-2 incorporates label consideration. Comparative analysis is conducted against outcomes obtained when all features are selected. The research scrutinizes the impact of feature selection on classifier performance, accuracy, and execution times, utilizing various machine learning algorithms such as Decision Tree (DT), K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB). The findings underscore that feature selection significantly enhances classifier accuracy while reducing execution times. Specifically, when all features were considered, only DT and KNN with 50 neighbors achieved 100% accuracy. However, with feature selection using Approach-1 (without labels), DT, K Nearest Neighbors, Support Vector Machine (excluding 100 neighbors), and Naive Bayes (with normal/Gaussian kernel) attained 100% accuracy. Employing Approach-2 (with labeled features), DT with 0.7 and 0.9 thresholds, SVM-G with all thresholds (0.6, 0.7, and 0.9), KNN with all thresholds (except 100 neighbors), and NB-n (with all thresholds) achieved 100% accuracy. The study emphasizes the pivotal role of feature selection in enhancing machine learning classifier performance, offering promising avenues for future research and practical applications across diverse domains.

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Section: Introductionmentioning

confidence: 99%