A Diagnosis Scheme of Gearbox Faults Based on Machine Learning and Motor Current Analysis

Bilal, El Yousfi; Soualhi, Abdenour; Medjaher, Kamal; Guillet, François

doi:10.1109/phm2022-london52454.2022.00045

Cited by 1 publication

(1 citation statement)

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“…To effectively manage the health and performance of bearings, it is essential to monitor and analyze various health indicators that provide insights into their condition. Health indicators are measurable quantities derived from sensor data such as electrical (Satish & Sarma, 2005;Schoen, Habetler, Kamran, & Bartfield, 1995;Abdenour, Kamal, Franc ¸ois, et al, 2022), acoustic (Elforjani & Shanbr, 2017), and vibration sensor data(K. Zhou & Tang, 2023), reflecting the degradation and fault development in bearings.…”

Section: Introductionmentioning

confidence: 99%

ANFIS-based Framework for the Prediction of Bearing’s Remaining Useful Life

Lourari,

Benkedjouh,

El Yousfi

et al. 2024

IJPHM

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Bearings are critical components extensively used in rotary machines, often being the leading cause of unexpected machine shutdowns. To mitigate system failures, it is crucial to implement effective maintenance strategies. This paper introduces a novel methodology for bearing prognostics, employing Wavelet Packet Decomposition (WPD) for data preprocessing, Sequential Backward Selection (SBS) for feature selection, and Adaptive Neuro-Fuzzy Inference System (ANFIS) networks for prognostic modeling. The proposed approach consists of two key steps. Firstly, the data undergoes preprocessing through Wavelet Packet Decomposition, enhancing the quality and extracting relevant features. Subsequently, the Remaining Useful Life (RUL) of the bearing is predicted using a degradation model. The accuracy of the proposed method is evaluated using a bearing life dataset obtained from a run-to-failure test (IMS dataset). The results demonstrate the remarkable capability of the ANFIS model to learn and accurately estimate the system’s RUL. By leveraging the combined power of WPD, SBS, and ANFIS, this methodology showcases its potential as an effective prognostic tool for bearing health assessment and proactive maintenance planning.

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