Health monitoring of bearing and gear faults by using a new health indicator extracted from current signals

Moncef Soualhi (1) , Thi Phuong Khanh Nguyen (1) , Abdenour Soualhi (2) , Kamal Medjaher (1) , Kamel Eddine Hemsas (3)

1 LGP - Laboratoire Génie de Production
2 LASPI - Laboratoire d'Analyse des Signaux et des Processus Industriels
3 UFAS1 - Université Ferhat-Abbas Sétif 1 [Sétif]

Gear reducer motors play an important role in industry due to their robustness and simplicity of construction. However, the appearance of faults in these systems can affect the quality of the product and lead to significant financial losses. Therefore, it is necessary to perform Prognostics and Health Management (PHM) for these systems. This paper aims to develop a practical and effective method allowing an early fault detection and diagnostic for critical components of the gear reducer, in particular gear and bearing defects. This method is based on a new indicator extracted from electrical signals. It allows characterizing different states of the gear reducer, such as healthy state, bearing faults, gear faults, and combined faults. The diagnostic of these states is done by the Adaptive Neuro-Fuzzy Inference System (ANFIS). The efficiency and the robustness of the proposed method are highlighted through numerous experimental tests with different levels of loads and speeds.

Keywords: Signal processing; Feature extraction; Health indicator; Machine learning; Artificial intelligence; Fault detection and diagnostics; Motor current signal analysis; Bearing and gear faults; Health monitoring

Domains: Automatic

Moncef Soualhi, Thi Phuong Khanh Nguyen, Abdenour Soualhi, Kamal Medjaher, Kamel Eddine Hemsas. Health monitoring of bearing and gear faults by using a new health indicator extracted from current signals. Measurement - Journal of the International Measurement Confederation (IMEKO), 2019, 141, pp.37-51. ⟨10.1016/j.measurement.2019.03.065⟩. ⟨hal-02134729⟩