Yanliang Ke

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The traditional approaches for condition monitoring of roller bearings are almost always achieved under Shannon sampling theorem conditions, leading to a big-data problem. The compressed sensing (CS) theory provides a new solution to the big-data problem. However, the vibration signals are insufficiently sparse and it is difficult to achieve sparsity using(More)
Compound faults often occur in rotating machinery, which increases the difficulty of fault diagnosis. In this case, blind source separation, which usually includes independent component analysis (ICA) and sparse component analysis (SCA), was proposed to separate mixed signals. SCA, which is based on the sparsity of target signals, was developed to sever the(More)
Vibration signals induced by faulty roller bearing usually contain much interference, which increases the difficulty of fault diagnosis. Thus, it is significant to enhance the fault features and carry out noise reduction. To achieve fault feature enhancement for roller bearing, a novel method based on Majorization-Minimization (MM) algorithm is developed in(More)
Vibration signals generated by faulty bearings often constitute “big data”, and it is therefore difficult to sparsely decompose them for dimensional reduction. In addition, vibration signals are generally buried in noise, especially at the initial fault stages. This increases the difficulty of determining roller bearing status. Therefore, it(More)
Multi-faults which usually exists in roller bearing makes fault diagnosis difficult. Thus, it is of great significance to carry out fault diagnosis of rotating machinery to ensure the complete machinery system to perform in a normal statement. To effectively separate the multi-faults and achieve the fault diagnosis, sparse component analysis based on the(More)
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