An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition

This paper suggests an automated approach for fault detection and classification in roller bearings, which is based on pattern recognition and principal components analysis of the measured vibration signals. The signals recorded are pre-processed applying a wavelet transform in order to extract the appropriate high frequency (detailed) area needed for ball bearing fault detection. This is followed by a pattern recognition (PR) procedure used to recognise between signals coming from healthy bearings and those generated from different bearing faults. Four categories of signals are considered, namely no fault signals (from a healthy bearing) inner race fault, outer race fault and rolling element fault signals. The PR procedure uses the first six principal components extracted from the signals after a proper principal component analysis (PCA). In this work a modified PCA is suggested which is much more appropriate for categorical data. The combination of the modified PCA and the PR method ensures that the fault is automatically detected and classified to one of the considered fault categories. The method suggested does not require the knowledge/ determination of the specific fault frequencies and/or any expert analysis: once the signal filtering is done and the PC's are found the PR method automatically gives the answer if there is a fault present and its type

An adaptive envelope analysis in a wireless sensor network for bearing fault diagnosis using fast kurtogram algorithm

This paper proposes a scheme to improve the performance of applying envelope analysis in a wireless sensor network for bearing fault diagnosis. The fast kurtogram is realized on the host computer for determining an optimum band-pass filter for the envelope analysis that is implemented on the wireless sensor node to extract the low frequency fault information. Therefore, the vibration signal can be monitored over the bandwidth limited wireless sensor network with both intelligence and real-time performance. Test results have proved that the diagnostic information for different bearing faults can be successfully extracted using the optimum band-pass filter

Condition monitoring of critical mechanical elements through Graphical Representation of State Configurations and Chromogram of Bands of Frequency

Fault detection is a crucial aspect to avoid catastrophic failures on mechanical systems, as well as to save money for companies. Currently, a number of non-destructing tests, signal processing analysis and artificial intelligence techniques are used for processing larger and larger amounts of maintenance data in all industry fields, either independently or combined. This manuscript presents a novel methodology for the condition monitoring of machinery, based on vibration analysis. The methodology is supported on two novel signal processing techniques: Graphical Representation of State Configurations (GRSC) and Chromogram of Bands of Frequency (CBF). These two new techniques apply basic concepts of the machine deterioration theory to the frequency spectrum. In order to prove the successful of the work presented, the methodology is tested against two real examples: vibration signals from the Case Western Reserve University (CWRU) Bearing Data Centre, and vibration signals from a high-speed train in normal operation. The results show that these new techniques can process large amounts of data without using artificial intelligence, identify adequately the operating condition of the tested systems and give precise information about that operating system by means of simple graphs and colours.The work is supported by the Spanish Government through the MAQ-STATUS DPI2015-69325-C2-1-r project

A Novel Method to Improve the Resolution of Envelope Spectrum for Bearing Fault Diagnosis Based on a Wireless Sensor Node

In this paper, an accurate envelope analysis algorithm is developed for a wireless sensor node. Since envelope signals employed in condition monitoring often have narrow frequency bandwidth, the proposed algorithm down-samples and cascades the analyzed envelope signals to construct a relatively long one. Thus, a relatively higher frequency resolution can be obtained by calculating the spectrum of the cascaded signal. In addition, a 50 % overlapping scheme is applied to avoid the distortions caused by Hilbert transform based envelope calculation. The proposed method is implemented on a wireless sensor node and tested successfully for detecting an outer race fault of a rolling bearing. The results show that the frequency resolution of the envelope spectrum is improved by 8 times while the data transmission remains at a low rate