Supervised diagnosis of induction motor faults: A proposed methodology for an improved performance evaluation

Martin-Diaz, Ignacio; Duque-Pérez, Óscar; Romero-Troncoso, René de Jesús; Moriñigo-Sotelo, Daniel

doi:10.1109/demped.2015.7303715

Cited by 6 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rotary machines category included degradation of bearings [50,51], gas circulator (GC) units used in the advanced gas-cooled reactor, and rotary machines in plant fan mills plates. Studies on exhaust fan, mill, and furnace fan; degradation of bearings of gas or wind turbine [52]; turbofan and induction motor [53], aircraft, and diesel engine [54,55]; and transformer short-circuit (considered in Electrical and Electronic) were also categorised.…”

Section:  mentioning

confidence: 99%

A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector

et al. 2021

View full text Add to dashboard Cite

The increasing availability of data, gathered by sensors and intelligent machines, is changing the way decisions are made in the manufacturing sector. In particular, based on predictive approach and facilitated by the nowadays growing capabilities of hardware, cloud-based solutions, and new learning approaches, maintenance can be scheduled—over cell engagement and resource monitoring—when required, for minimizing (or managing) unexpected equipment failures, improving uptime through less aggressive maintenance schedules, shortening unplanned downtime, reducing excess (direct and indirect) cost, reducing long-term damage to machines and processes, and improve safety plans. With access to increased levels of data (and over learning mechanisms), companies have the capability to conduct statistical tests using machine learning algorithms, in order to uncover root causes of problems previously unknown. This study analyses the maturity level and contributions of machine learning methods for predictive maintenance. An upward trend in publications for predictive maintenance using machine learning techniques was identified with the USA and China leading. A mapping study—steady set until early 2019 data—was employed as a formal and well-structured method to synthesize material and to report on pervasive areas of research. Type of equipment, sensors, and data are mapped to properly assist new researchers in positioning new research activities in the domain of smart maintenance. Hence, in this paper, we focus on data-driven methods for predictive maintenance (PdM) with a comprehensive survey on applications and methods until, for the sake of commenting on stable proposal, 2019 (early included). An equal repartition between evaluation and validation studies was identified, this being a symptom of an immature but growing research area. In addition, the type of contribution is mainly in the form of models and methodologies. Vibrational signal was marked as the most used data set for diagnosis in manufacturing machinery monitoring; furthermore, supervised learning is reported as the most used predictive approach (ensemble learning is growing fast). Neural networks, followed by random forests and support vector machines, were identified as the most applied methods encompassing 40% of publications, of which 67% related to deep neural network with long short-term memory predominance. Notwithstanding, there is no robust approach (no one reported optimal performance over different case tests) that works best for every problem. We finally conclude the research in this area is moving fast to gather a separate focused analysis over the last two years (whenever stable implementations will appear).

show abstract

Section:  mentioning

confidence: 99%

A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The accuracy measure does not allow a correct interpretation of the classifier performance with each class taken into account, which it is an important fact when discriminating among different severity degrees. In this sense, the use of additional performance metrics is required [29], [30] to appreciate the differences among various classifiers for every damaged rotor condition, and under imbalanced conditions. The scores used are the following:…”

Section: A Performance Analysis Of the Proposed Approachmentioning

confidence: 99%

Early Fault Detection in Induction Motors Using AdaBoost With Imbalanced Small Data and Optimized Sampling

Martin-Diaz

Moriñigo-Sotelo

Duque-Pérez

et al. 2017

IEEE Trans. on Ind. Applicat.

Self Cite

110

View full text Add to dashboard Cite

“…One of the key factors of overall efficiency maximization covers the well-sized and high-efficient components [19][20][21][22]. Therefore, the reduction and prediction of faults occurring in electrical machines and drive systems such as electrical, thermal, and mechanical faults of electrical machines are strongly suggested to be essential [23][24][25][26][27][28][29]. Classical solutions of Fault Detection and Diagnosis (FDD) [30] are based on the complex mathematical models, e.g., supervised diagnosis [25][26][27][28][29]31], or dynamic models [32][33][34][35][36][37] of the processing system.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the reduction and prediction of faults occurring in electrical machines and drive systems such as electrical, thermal, and mechanical faults of electrical machines are strongly suggested to be essential [23][24][25][26][27][28][29]. Classical solutions of Fault Detection and Diagnosis (FDD) [30] are based on the complex mathematical models, e.g., supervised diagnosis [25][26][27][28][29]31], or dynamic models [32][33][34][35][36][37] of the processing system. Intelligent modernization has contributed to the widespread use of Machine Learning (ML) techniques in industrial applications [38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

et al. 2019

View full text Add to dashboard Cite

Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

show abstract

Supervised diagnosis of induction motor faults: A proposed methodology for an improved performance evaluation

Cited by 6 publications

References 44 publications

A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector

A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector

Early Fault Detection in Induction Motors Using AdaBoost With Imbalanced Small Data and Optimized Sampling

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

Contact Info

Product

Resources

About