Model-based fault diagnosis in electric drives using machine learning

Murphey, Yi Lu; Masrur, M. Abul; Chen, ZhiHang; Zhang, Baifang

doi:10.1109/tmech.2006.875568

Cited by 132 publications

(45 citation statements)

References 26 publications

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“…A review of intelligent techniques applied for fault diagnosis in power electronics is presented in [43]. A model-based diagnostic system trained with the machine learning technology for inverters feeding electric drives is presented in [44].…”

Section: Power Electronics Development and Device/ Component Level DImentioning

confidence: 99%

Model-based methodology using modified sneak circuit analysis for power electronic converter fault diagnosis

Soman

Davidson

McArthur

et al. 2012

IET Power Electron.

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This study presents a novel research methodology and approach for diagnosing pertinent failures for power electronic converters. The concept of the generalised connection matrix for a sneak circuit analysis is used in this research. A novel modification to this process shown in this study produces a usable feature that provides unique signatures under fault scenarios and could be used to diagnose faults. The novelty lies in using component currents to form the generalised connection matrix. This study presents the results obtained using data collected from fault scenarios on a hardware set-up for the dc -dc buck converter.

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Section: Power Electronics Development and Device/ Component Level DImentioning

confidence: 99%

Model-based methodology using modified sneak circuit analysis for power electronic converter fault diagnosis

Soman

Davidson

McArthur

et al. 2012

IET Power Electron.

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“…Step 4 Based on the error between the set-point value T 1 and the soft-sensing value T of the furnace temperature, the original set-point of heating gas flow u s is given by (10);…”

Section: Structure and Functionmentioning

confidence: 99%

“…Examples of such control methods are the intelligent control of exit temperature in a gas-fuel can-type combustor [6], and the supervisory control of furnace temperature [7]. Also, some applied cases of process control [8,9], fault diagnosis [10] and process variable soft-sensing [11] have exhibited the prominent aspects of both intelligent control and routine control. However, the applications of similar methods on the combustion control of a shaft ore-roasting furnace have rarely been reported by far.…”

Section: Introductionmentioning

confidence: 99%

Hybrid intelligent control of combustion process for ore-roasting furnace

Yan

Chai

et al. 2008

J. Control Theory Appl.

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Because of its synthetic and complex characteristics, the combustion process of the shaft ore-roasting furnace is very difficult to control stably. A hybrid intelligent control approach is developed which consists of two systems: one is a cascade fuzzy control system with a temperature soft-sensor, and the other is a ratio control system for air flow with a compensation model for heating gas flow and air-fuel ratio. This approach combined intelligent control, soft-sensing and fault diagnosis with conventional control. It can adjust both the heating gas flow and the air-fuel ratio in real time. By this way, the difficulty of online measurement of the furnace temperature is solved, the fault ratios during combustion process is decreased, the steady control of the furnace temperature is achieved, and the gas consumption is reduced. The successful application in shaft furnaces of a mineral processing plant in China indicates its effectiveness.

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“…[14]. Although these studies have shown the advantages of AI-based approaches for induction motor fault diagnosis, most of these approaches are based on supervised learning, in which high quality training data with good coverage of true failure conditions are required to perform model training [15]. However, it is not easy to obtain sufficient labelled fault data to train the model in practice.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

Shao

Sun

Yan

et al. 2017

Chin. J. Mech. Eng.

135

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Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-bylayer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

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Model-based fault diagnosis in electric drives using machine learning

Cited by 132 publications

References 26 publications

Model-based methodology using modified sneak circuit analysis for power electronic converter fault diagnosis

Model-based methodology using modified sneak circuit analysis for power electronic converter fault diagnosis

Hybrid intelligent control of combustion process for ore-roasting furnace

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

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