Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction

Abstract

Fault prediction is the key technology of the predictive maintenance. Currently, researches on fault prediction are mainly focused on the evaluation of the intensities of the failure and the remaining life of the machine. There is lack of methods on the prediction of fault locations and fault characters. To satisfy the requirement of the prediction of the fault characters, the data acquisition and fusion strategies were studied. Firstly, the traditional vibration measurement mechanism and its disadvantages were presented. Then, the full-vector data acquisition and fusion model were proposed. After that, the sampling procedure and information fusion algorithm were analyzed. At last, the fault prediction method based on full-vector spectrum was proposed. The methodology is that of Dr. Bently and Dr. Muszynska. On the basis of this methodology, the application study has been carried out. The uncertainty of the spectrum structure can be eliminated by the designed data acquisition and fusion method. The reliability of the diagnosis on fault character was improved. The study on full-vector data acquisition system laid the technical foundation for the prediction and diagnosis research of the fault characters.

Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum

“…According to the theory of full vector spectrum, the main vector of vibration harmonic can be simplified as a simple calculation by FFT [1,2]. Assuming that and are the signals acquired by the transducers and .…”

“…Prediction modeling and vibration data feature extraction are the key predictive maintenance technologies. In recent years, scholars have conducted lots of work on the aspect of equipment fault prediction, and many prediction methods are proposed [2][3][4][5][6]. These methods can be roughly divided into three categories: knowledge based, model based, and data based.…”

An incipient fault diagnosis method for rotating machinery based on bilateral spectrum and precession energy difference density spectrum

“…Therefore, the traditional spectrum analysis technology still has important research significance and a very wide range of applications in the rotating machinery fault diagnosis for its simplicity and intuitive [13][14][15]. The most representative spectral analysis technologies are holospectrum technology [16][17][18], full-spectrum technology [19] and vector spectrum technology [20,21]. These three technologies have their respective merits and have been widely used in engineering.…”

Maintainability analysis of mining trucks with data analytics.

“…This method also shows that the reliability of the diagnosis on fault character was improved, and it gives the technical foundation for the prediction and diagnosis research of the fault characters (Chen, Han, Lei, Cui, & Guan, 2016). …”

“…A theoretical study by Chen et al (2016), predicted faults from the data acquisition and fusion strategies, and it used the fault prediction method based on full-vector spectrum which belongs to Dr. Bently and Dr. Muszynska. According to this method, the uncertainty of the spectrum structure can be extracted by the designed data acquisition and fusion…”