Multi-label Fault Classification Experiments in a Chemical Process

Rauber, Thomas W.; Mello, Lucas Henrique Sousa; Rocha, Victor F.; Varejão, Flávio Miguel

doi:10.1109/bracis.2014.55

Cited by 2 publications

(1 citation statement)

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“…The concept is to turn the original multi-label set into binary sets or multi-class sets that adequately can be processed with the classical algorithms. Besides binarization with the widely applied binary relevance technique, also the voting methods and divide-and-conquer approaches are applied to accomplish multi-label transformation [100,103,104]. Such separated sets are learned by single-label classifiers, such as decision trees.…”

Section: Brief Introduction Of Multi-label Classification Methodsmentioning

confidence: 99%

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

et al. 2019

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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.

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Section: Brief Introduction Of Multi-label Classification Methodsmentioning

confidence: 99%