scholarly journals Rolling bearing fault detection based on vibration signal analysis and cumulative sum control chart

2021 ◽  
Vol 49 (3) ◽  
pp. 684-695
Author(s):  
Jawad Mohammed ◽  
Jaber Abdulhady
Keyword(s):  
Fault Detection ◽  
Control Chart ◽  
Time Domain ◽  
Signal Analysis ◽  
Detection System ◽  
Significant Feature ◽  
Test Rig ◽  
Cumulative Sum ◽  
Analysis Technique ◽  
Cumulative Sum Control

Monitoring the condition of rotating machines is essential for the systems' safety, reducing maintenance costs, and increasing reliability. In this research, a fault detection system for bearings was developed using the vibration analysis technique with the statistical control chart approach. A test rig was first designed and constructed; then, various bearing faults, such as inner race and outer race faults, were simulated and examined in the test rig. After capturing the vibration signals at different bearing health conditions, the time-domain signal analysis technique was employed for extracting different indicative features. The obtained time domain features were then analyzed to find out the most fault-significant feature. Then, only one feature was selected to design the control chart for bearing health condition monitoring. The cumulative sum control chart (CUSUM was utilized since it can detect the small changes in bearing health states. The results showed the effectiveness of utilizing this method, and it was found that the percentage of the out-of-control points in the event of the combined cage and ball fault to the number of tested samples is greater than the other fault types.

Multivariate cumulative sum control chart and measure of process capability based on bivariate ranked set schemes

2020 ◽  
Vol 150 ◽  
pp. 106891
Author(s):  
Rashid Mehmood ◽  
Muhammad Hisyam Lee ◽  
Iftikhar Ali ◽  
Muhammad Riaz ◽  
Shahid Hussain
Keyword(s):  
Control Chart ◽  
Process Capability ◽  
Cumulative Sum ◽  
Cumulative Sum Control

scholarly journals Risk-adjusted zero-inflated Poisson CUSUM charts for monitoring influenza surveillance data

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Yueying Tan ◽  
Xin Lai ◽  
Jiayin Wang ◽  
Xuanping Zhang ◽  
Xiaoyan Zhu ◽  
...  
Keyword(s):  
Risk Factors ◽  
False Alarm ◽  
Control Chart ◽  
Control Charts ◽  
Poisson Model ◽  
Surveillance Data ◽  
Impact Factors ◽  
Influenza Surveillance ◽  
Cumulative Sum ◽  
Cumulative Sum Control

Abstract Background The influenza surveillance has been received much attention in public health area. For the cases with excessive zeroes, the zero-inflated Poisson process is widely used. However, the traditional control charts based on zero-inflated Poisson model, ignore the association between influenza cases and risk factors, and thus may lead to unexpected mistakes when implementing monitoring charts. Method In this paper, we proposed risk-adjusted zero-inflated Poisson cumulative sum control charts, in which the risk factors were put to adjust the risk of influenza and the adjustment was made by zero-inflated Poisson regression. We respectively proposed the control chart monitoring the parameters individually and simultaneously. Results The performance of our proposed risk-adjusted zero-inflated Poisson cumulative sum control chart was evaluated and compared with the unadjusted standard cumulative sum control charts in simulation studies. The results show that for different distribution of impact factors and different coefficients, the risk-adjusted cumulative sum charts can generate much less false alarm than the standard ones. Finally, the influenza surveillance data from Hong Kong is used to illustrate the application of the proposed chart. Conclusions Our results suggest that the adjusted cumulative sum control chart we proposed is more accurate and credible than the unadjusted standard control charts because of the lower false alarm rate of the adjusted ones. Even the unadjusted control charts may signal a little faster than the adjusted ones, the alarm they raise may have low credibility since they also raise alarm frequently even the processes are in control. Thus we suggest using the risk-adjusted cumulative sum control charts to monitor the influenza surveillance data to alert accurately, credibly and relatively quickly.

scholarly journals Wireless Fault Detection System for an Industrial Robot Based on Statistical Control Chart

2017 ◽  
Vol 7 (6) ◽  
pp. 3421
Author(s):  
Alaa Abdulhady Jaber ◽  
Robert Bicker
Keyword(s):  
Fault Detection ◽  
Control Chart ◽  
Production Systems ◽  
Detection System ◽  
Industrial Robot ◽  
Vibration Signal ◽  
Cost Effective ◽  
Base Station ◽  
Industrial Robots ◽  
Statistical Control

Industrial robots are now commonly used in production systems to improve productivity, quality and safety in manufacturing processes. Recent developments involve using robots cooperatively with production line operatives. Regardless of application, there are significant implications for operator safety in the event of a robot malfunction or failure, and the consequent downtime has a significant impact on productivity in manufacturing. Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation and thus reducing the maintenance costs. Developments in electronics and computing have opened new horizons in the area of condition monitoring. The aim of using wireless electronic systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to develop an online and wireless fault detection system for an industrial robot based on statistical control chart approach. An experimental investigation was accomplished using the PUMA 560 robot and vibration signal capturing was adopted, as it responds immediately to manifest itself if any change is appeared in the monitored machine, to extract features related to the robot health conditions. The results indicate the successful detection of faults at the early stages using the key extracted parameters.

Sign in / Sign up

Export Citation Format

Share Document