Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.

Bearing defect diagnosis and acoustic emission

Abstract: Acoustic emission (AE) was originally developed for non-destructive testing of static structures, but over the years its application has been extended to health monitoring of rotating machines and bearings. It offers the advantage of earlier defect detection in comparison with vibration analysis. However, limitations in the successful application of the AE technique for monitoring bearings have been partly due to the difficulty in processing, interpreting and classifying the acquired data. The investigation reported in this paper was centred on the application of standard AE characteristic parameters on a radially loaded bearing. An experimental test rig was modified such that defects could be seeded onto the inner and outer races of a test bearing. As the test rig was adapted for this purpose, it offered high background acoustic emission noise providing a realistic test for fault diagnosis. In addition to a review of current diagnostic methods for applying AE to bearing diagnosis, the results of ...

Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds.

Abstract: Vibration analysis is an effective tool for the condition monitoring and fault diagnosis of rolling element bearings. Conventional diagnostic methods are based on the stationary assumption, thus they are not applicable to the diagnosis of bearings working under varying speed. This constraint limits the bearing diagnosis to the industrial application significantly. In order to extend the conventional diagnostic methods to speed variation cases, a tacholess envelope order analysis technique is proposed in this paper. In the proposed technique, a tacholess order tracking (TLOT) method is first introduced to extract the tachometer information from the vibration signal itself. On this basis, an envelope order spectrum (EOS) is utilized to recover the bearing characteristic frequencies in the order domain. By combining the advantages of TLOT and EOS, the proposed technique is capable of detecting bearing faults under varying speeds, even without the use of a tachometer. The effectiveness of the proposed method is demonstrated by both simulated signals and real vibration signals collected from locomotive roller bearings with faults on inner race, outer race and rollers, respectively. Analyzed results show that the proposed method could identify different bearing faults effectively and accurately under speed varying conditions.

Bearing fault detection via autoregressive stator current modeling

Abstract: This research proposes a method for detecting developing bearing faults via stator current Current-based condition monitoring offers significant economic savings and implementation advantages over vibration-based techniques This method begins by filtering the stator current to remove most of the significant frequency content unrelated to bearing faults Afterwards, the filtered stator current is used to train an autoregressive signal model This model is first trained while the bearings are healthy, and a baseline spectrum is computed As bearing health degrades, the modeled spectrum deviates from its baseline value; the mean spectral deviation is then used as the fault index This fault index is able to track changes in machine vibration due to developing bearing faults Due to the initial filtering process, this method is robust to many influences including variations in supply voltage, cyclical load torque variations, and other (nonbearing) fault sources Experimental results from 10 different bearings are used to verify the proficiency of this method