Condition monitoring

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

A New Bearing Fault Detection Method in Induction Machines Based on Instantaneous Power Factor

Abstract: Fault detection and diagnosis of asynchronous machine has become a central problem in industry over the past decade. A solution to tackle this problem is to use stator current for a condition monitoring, referred to as motor current signature analysis. This paper argues that bearing faults would have a negligible effect on motor currents and instead argues that the more likely reason why the faults can be detected in currents is because they entail a fluctuating resistive torque which acts immediately, in contrast to the radial displacement which takes time to integrate to a perceptible displacement even in response to a step change in velocity. In this context, we propose a new method for detecting bearing defects based on the exploitation of the instantaneous power factor that varies according to torque oscillations. Experimental results show the good performances of the proposed method which will be compared with the instantaneous power method to highlight the feasibility and advantages of this method.

Review of Condition Monitoring of Rolling Element Bearing Using Vibration Analysis and Other Techniques

Abstract: Different types of machines having rotary component are linked together in process industries, to perform the process of manufacturing. The failure of any single machine rotary component in the process can result in loss of money per downtime hour. To continue the working of the machines, it is necessary to monitor the health of the machine during its operation. Bearing failure is considered to be one of the major causes of breakdown in different rotating machines operating in industry at high and low speeds. Vibration in machines is generated due to defective bearings. During use, the condition of the bearings change, hence the vibrations also change and its characteristic basically depends on the cause. Hence, this characteristic feature of the bearing makes it suitable for vibration monitoring and other monitoring techniques. This article presents the brief review of recent trends in research on bearing defects, sources of vibration and vibration measurement techniques in the time domain, frequency domain and time frequency domain. The study reveals that, the envelope analysis method and time–frequency analysis technique are able to detect the bearing fault effectively. The wavelet analysis technique along with artificial neural network and fuzzy logic is also found to be the most effective techniques for fault analysis in rolling element bearing. Finally, it is concluded that vibration analysis technique along with other condition monitoring techniques gives better results in the fault diagnostics and condition monitoring of rolling element bearing.

Road condition monitoring system based on a stereo camera

Abstract: This paper presents a method and evaluation results to monitor and detect road conditions (ice, water, snow and dry asphalt). The developed device bases on light polarization changes when reflected from road surface. The recognition capability has been improved with texture analysis which estimates contrast content of an image. Test drives were performed with a vehicle equipped also with a commercial solution from Vaisala which was used as the reference sensor. The results show that the proposed solution does not currently adapt to different conditions perfectly well. Therefore, further development targets have been identified to include not only more adjustable classifier but externally lighting to stabilizing ambient illumination.

A weighted multi-scale morphological gradient filter for rolling element bearing fault detection.

Abstract: This paper presents a novel signal processing scheme, named the weighted multi-scale morphological gradient filter (WMMG), for rolling element bearing fault detection. The WMMG can depress the noise at large scale and preserve the impulsive shape details at small scale. Both a simulated signal and vibration signals from a bearing test rig are employed to evaluate the performance of the proposed technique. The traditional envelope analysis and a multi-scale enveloping spectrogram algorithm combining continuous wavelet transform and envelope analysis (WT-EA) are also studied and compared with the presented WMMG. Experimental results have demonstrated the effectiveness of the WMMG to extract the impulsive components from the raw vibration signal with strong background noise. We also investigated the classification performance on identifying bearing faults based on the WMMG and statistical parameters with varied noise levels. Application results reveal that the WMMG achieves the same or better performance as EA and WT-EA. Meanwhile, the WMMG requires low computation cost and is very suitable for on-line condition monitoring of bearing operating states.