Bearing fault diagnosis based on intrinsic time-scale decomposition and improved Support vector machine model

In order to achieve the bearing fault diagnosis so as to ensure the steadiness of rotating machinery. This article proposed a model based on intrinsic time-scale decomposition (ITD) and improved support vector machine method (ISVM), so as to deal with the non-stationary and nonlinear characteristics of bearing vibration signals. Firstly, the feature extraction method intrinsic time-scale decomposition (ITD) is used and the energy entropy are extracted so as to process the vibration signal in this paper. Then, the local tangent space alignment (LTSA) method is introduced to extract the characteristic features and reduce the dimension of the selected entropy features. Finally, the features are used to train the ISVM model as to classify bearings defects. Cases of actual were analyzed. The results validate the effectiveness of the proposed algorithm

Bearing fault diagnosis based on intrinsic time-scale decomposition and improved Support vector machine model

In order to achieve the bearing fault diagnosis so as to ensure the steadiness of rotating machinery. This article proposed a model based on intrinsic time-scale decomposition (ITD) and improved support vector machine method (ISVM), so as to deal with the non-stationary and nonlinear characteristics of bearing vibration signals. Firstly, the feature extraction method intrinsic time-scale decomposition (ITD) is used and the energy entropy are extracted so as to process the vibration signal in this paper. Then, the local tangent space alignment (LTSA) method is introduced to extract the characteristic features and reduce the dimension of the selected entropy features. Finally, the features are used to train the ISVM model as to classify bearings defects. Cases of actual were analyzed. The results validate the effectiveness of the proposed algorithm

A modelling-oriented scheme for control chart pattern recognition

Control charts are graphical tools that monitor and assess the performance of production processes, revealing abnormal (deterministic) disturbances when there is a fault. Simple patterns belonging to one of six types can be observed when a fault is occurring, and a Normal pattern when the process is performing under its intended conditions. Machine Learning algorithms have been implemented in this research to enable automatic identification of simple patterns. Two pattern generation schemes (PGS) for synthesising patterns are proposed in this work. These PGSs ensure generality, randomness, and comparability, as well as allowing the further categorisation of the studied patterns. One of these PGSs was developed for processes that fulfil the NIID (Normally, identically and independently distributed) condition, and the other for three first-order lagged time series models. This last PGS was used as base to generate patterns of feedback-controlled processes. Using the three aforementioned processes, control chart pattern recognition (CCPR) systems for these process types were proposed and studied. Furthermore, taking the recognition accuracy as a performance measure, the arrangement of input factors that achieved the highest accuracies for each of the CCPR systems was determined. Furthermore, a CCPR system for feedback-controlled processes was developed