Condition monitoring

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

2nd International Conference on Innovations in Automation and Mechatronics Engineering, ICIAME 2014 Fault size estimation in the outer race of ball bearing using discrete wavelet transform of the vibration signal

Abstract: Vibration signal analysis being one of the widely acceptable tools for bearing condition monitoring; the estimation of fault size from vibration signal is still a challenge and a subject of interest to the researchers. This work presents the decomposition of the vibration signal by using discrete wavelet transform assisted by sym5 wavelet. Symlet wavelet has a linear phase nature which maintains sharpness in the signal even when there is sudden change in the signal. The decomposed signal evidently splits the peak corresponding to the ball entry into and exit from the fault, enabling in an estimation of the defect size present in the bearing. Experiments conducted for different sizes of the defect present on the outer race of deep groove ball bearing affirm the efficacy of the applied technique for different vibration signals. The output of the proposed technique finds close correlation with the actual defect size measured from optical microscope with the maximum deviation in the result of 2.06%.

Advanced Diagnosis of Stator Turn-to-Turn Faults and Static Eccentricity in Induction Motors Based on Internal Flux Measurement

Abstract: Large induction motors used in variable speed industrial applications (digging, traction, etc.) undergo dynamic stresses at high power levels. Their lifetime is reduced as compared with constant speed motors and downtime costs are very high. To monitor the condition of such motors, advanced sensor instrumentation is considered, including installation of Hall effect flux sensors inside the motor airgap. Such an advanced instrumentation enables continuous measurement of the airgap flux in both time and space domains simultaneously. This allows for advanced fault detection algorithms. This paper presents a method for early diagnosis of stator turn-to-turn faults and static eccentricity, and differentiation between these two conditions. The paper demonstrates that the proposed method detects, localizes, and quantifies both faults with high accuracy and sensitivity. Furthermore, each fault can be detected at an incipient stage and its development can be predicted. The results of the presented study have been supported by both simulation and experimental results obtained on a laboratory scale induction motor. Based on the results presented in the paper, a real-time condition monitoring system has been developed.

RoadMonitor: An Intelligent Road Surface Condition Monitoring System

Abstract: Well maintained road network is an essential requirement for the safety and consistency of vehicles moving on that road and the wellbeing of people in those vehicles. On the other hand, guaranteeing an adequate maintenance by road managers can be achieved via having sufficient and accurate information concerning road infrastructure quality that can be as well utilized concurrently by the widespread means of users’ mobile devices both locally and worldwide. This article proposes a road condition monitoring framework that detects the road anomalies such as speed bumps. In the proposed approach, the main indicator for road anomalies is the gyroscope around gravity rotation in addition to the accelerometer sensor as a cross-validation method to confirm the detection results that were gathered from the gyroscope.

Multivariable Trend Analysis Using Neural Networks for Intelligent Diagnostics of Rotating Machinery

Abstract: The objective of this paper is the development of an efficient intelligent diagnostic procedure that considers several diagnostic indices for the quantification of developing faults and for monitoring machine condition. In this procedure, the condition monitoring is performed based on the on-line vibration measurements, and further, the fault quantification is formulated into a multivariate trend analysis. Self-organizing neural networks are then deployed to perform the multivariable trending of the fault development. The attributes for the disordering of “knots” in the trend analysis are determined. The disordering of neural network units is then eliminated by suitably altering the self-organizing neural network algorithm. Applications of this diagnostic procedure to the condition monitoring and life estimation of a bearing system are fully developed and demonstrated. The efficiency and advantages of the intelligent diagnostic procedure in precisely monitoring and quantifying the fault development are systematically brought out considering this bearing system.