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Degradation trend estimation of rolling bearing is widely applied in many engineering applications. With the rapid development of sensors, massive data will be acquired because of the monitoring of machinery. So how to extract and use the effective data from the big data for trend estimation has profound research value. The least square support vector machine (LSSVM), which is a kind of novel artificial...
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 sparsity of objective signals was proposed...
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 this study. First, a sparsity optimization...
Aiming at the problems of fault diagnosis for rotating machinery, this paper proposed a fault diagnosis method combining minimum entropy deconvolution (MED) with fruit fly optimization algorithm (FOA). In the MED method, the objective function method (OFM) is used to find the set of filter coefficients under the condition of maximal kurtosis. Given that the filter coefficients obtained by OFM are...
This paper proposes an intelligent diagnosis method for condition diagnosis of rotating machinery by using wavelet transform (WT) and ant colony optimization (ACO), in order to detect faults and distinguish fault types at an early stage. The WT is used to extract a feature signal of each machine state from a measured vibration signal for for high-accuracy condition diagnosis. The decision method of...
A novel diagnosis method is proposed based on the Kullback-Leibler (KL) divergence information for fault diagnosis of the diesel engine. A skewness wave (SW) is defined in the time domain using the vibration signal, and a method to obtain the skewness information wave (SIW) is also proposed. Practical example of diagnosis for a bearing used in a diesel engine is provided to verify the effectiveness...
In order to extract the features from the fault signal highly contaminated by the noise, and accurately identify the fault types, a novel feature extraction method is proposed based on the statistic features and information divergence for the condition diagnosis of reciprocating machinery. A root mean square (RMS) wave, called as the ??RW??, is defined in the time domain using the vibration signal...
In the case of fault diagnosis of the plant machinery, knowledge for distinguishing faults is ambiguous because definite relationships between symptoms and fault types cannot be easily identified. So this paper presents a sequential diagnosis method for rolling bearing by a fuzzy neural network with the features of a vibration signal in time domain. The fuzzy neural network is realized with a developed...
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