ArticleSensors (Basel, Switzerland)2024
Fault Diagnosis of a Multistage Centrifugal Pump Using Explanatory Ratio Linear Discriminant Analysis.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Physics guided fused image learning with enhanced squeeze excitation for failure analysis of multistage centrifugal pumps.Scientific reports · 2026Article
- Multi-sensor observer-based residual learning with Auto-Permutation Feature Importance for fault diagnosis of multistage centrifugal pumps under variable pressures.Scientific reports · 2025Article
- Machine learning-based monitoring and design of managed aquifer rechargers for sustainable groundwater management: scope and challenges.Environmental science and pollution research international · 2025Review
- Distributed Sparse Manifold-Constrained Optimization Algorithm in Linear Discriminant Analysis.Journal of imaging · 2025Article
- Advanced Bearing-Fault Diagnosis and Classification Using Mel-Scalograms and FOX-Optimized ANN.Sensors (Basel, Switzerland) · 2024Article
- Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
This study introduces an innovative approach for fault diagnosis of a multistage centrifugal pump (MCP) using explanatory ratio (ER) linear discriminant analysis (LDA). Initially, the method addresses the challenge of background noise and interference in vibration signals by identifying a fault-sensitive frequency band (FSFB). From the FSFB, raw hybrid statistical features are extracted in time, frequency, and time-frequency domains, forming a comprehensive feature pool. Recognizing that not all features adequately represent MCP conditions and can reduce classification accuracy, we propose a novel ER-LDA method. ER-LDA evaluates feature importance by calculating the explanatory ratio between interclass distance and intraclass scatteredness, facilitating the selection of discriminative features through LDA. This fusion of ER-based feature assessment and LDA yields the novel ER-LDA technique. The resulting selective feature set is then passed into a k-nearest neighbor (K-NN) algorithm for condition classification, distinguishing between normal, mechanical seal hole, mechanical seal scratch, and impeller defect states of the MCP. The proposed technique surpasses current cutting-edge techniques in fault classification.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.