Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis
نویسندگان
چکیده
منابع مشابه
Feature Extraction and Selection for Automatic Fault Diagnosis of Rotating Machinery
In this work we present three feature extraction models used in vibratory data from rotating machinery for bearing fault diagnosis. Vibrations signals are acquired by accelerometers which are then submitted to different feature extraction modules. Our tests suggest that pooling heterogeneous feature sets achieve better results than using a single extraction model. Besides, different classifiers...
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Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represente...
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A continuing task in engineering is to increase the reliability, availability and safety of technical processes and to achieve these fault diagnosis becomes an advanced supervision tool in the present industries. Vibration in rotating machinery is mostly caused by unbalance, misalignment, shaft crack, mechanical looseness and other malfunctions. The objective of this paper is to propose a model...
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ژورنال
عنوان ژورنال: IEEE Transactions on Cybernetics
سال: 2020
ISSN: 2168-2267,2168-2275
DOI: 10.1109/tcyb.2020.3032945