Adaptive neuro-fuzzy inference system for bearing fault detection in induction motors using temperature, current, vibration data

A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions

“…Recently, artificial intelligence (AI) has been introduced into the fault diagnosis process for condition monitoring, including techniques based on fuzzy logic (FL) (Xu et al 2009), neural networks (NN) (Mahammed and Hiyama 2011), genetic algorithms (GA) (Samanta et al 2004), adaptive neuro fuzzy inference systems (ANFIS) (Yilmaz and Ayaz 2009) and support vector machines (SVM) (Sugumaran and Ramachandran 2011). AI aims to generate classifying expressions simple enough to be understood easily by humans (Michie et al 2009).…”

A Review Study on Mathematical Methods for Fault Detection Problems in Induction Motors

“…The first AR coefficient and variance of white noise input to drive the AR model is increased with the aging (Fig.14 and Fig.15). Except from vibration and current signals, bearing temperature gives valuable information about the health of the bearings (17) since temperature is an important factor for lubricant. High temperatures reduce the viscosity of lubricant inside of the bearing, and cause early bearing failure.…”

“…(1)(2)(3)(4)(5)(6)(7) This paper addresses fault detection methods in induction motors together with theory and applications on experimental data acquired during performance test of the motors subjected to accelerated aging (6)(7) . Detection of eccentricity fault (7)(8)(9) , bearing fault (10)(11)(12)(13)(14)(15)(16)(17)(18) , and stator insulation fault (19)(20) is considered. Applications of statistical methods, power spectral density analysis, coherence analysis, continuous and discrete wavelet transform, autoregressive modeling method, adaptive neuro-fuzzy inference system, artificial neural network is presented by means of the experimental data.…”

Multiclassificador inteligente de falhas no domínio do tempo em motores de indução trifásicos alimentados por inversores de frequência

“…O rolamento é o principal componente dos MIT associados a falhas e consequentes paradas indesejadas de processo. Falhas neste componente podem ser diagnosticadas de forma não-invasiva por intermédio do monitoramento quantitativo de variáveis como vibração, corrente, temperatura, velocidade entre outros (BELLINI et al, 2008;YILMAZ; No artigo de YANG, Yi-long e GONG (2001) são descritos os efeitos do parasitismo capacitivo em motores quando alimentados por inversores de frequência. Dentre as principais faltas relacionadas com o parasitismo destacam-se a queima de enrolamentos, o desgaste prematuro dos rolamentos, o surgimento de correntes de fuga com o terra e as distorções harmônicas de corrente.…”