Statistical Process Control Charts Applied to Steelmaking Quality Improvement

El-Din, M. A. Sharaf; Rashed, H. I.; Elkhabeery, M. M.

doi:10.1080/16843703.2006.11673127

Cited by 14 publications

(10 citation statements)

References 1 publication

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“…These rules also have negative effect if all of them are applied, since they greatly increase the number of false alarms (Montgomery and Runger, 2014;El-Din et al, 2006). Thus, few of them are going to be utilized here for the robot fault detection.…”

Section: Performance Testing Of the Designed Control Chartmentioning

confidence: 99%

“…In addition to the three-sigma limits, additional warning limits can be utilized. These are named the inner limits, usually placed at two-sigma (El-Din et al, 2006).…”

Section: Control Chart Limitsmentioning

confidence: 99%

“…A comparison of these features, to investigate which is the most faults sensitive one, was accomplished. The sensitive feature must be normally distributed, as this is a condition that has to be met in order to calculate the threshold values using SCC (Montgomery and Runger, 2014;El-Din et al, 2006). The STD feature, which is normally used as a measure of extent of variation of the processed data and has the same units as the data, was found to be the most faults sensitive and normally distributed one; and hence it was selected for comparison among fault severities.…”

Section: Feature Extractionmentioning

confidence: 99%

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Industrial Robot Fault Detection Based on Statistical Control Chart

Jaber

Bicker

2016

American Journal of Engineering and Applied Sciences

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Industrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. There are significant implications for operator safety in the event of a robot malfunction or failure and an unforeseen robot stoppage due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. In this research a fault detection system based on statistical control chart has been designed. An experimental investigation was accomplished using the PUMA 560 robot. Vibration signals are captured from the robot when it executes a repetitive task and then some statistical features are extracted from the signals, by utilising a developed data acquisition system based on National instruments hardware and software. The extracted vibration features, which are related to the robot healthy and faulty states, have subsequently been used for building and testing a statistical control chart. The chart has been validated using part of the measured data set, not used within the design stage, which represents the robot operating conditions. Validation results indicate the successful detection of faults at the early stages using the key extracted parameters.

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Section: Performance Testing Of the Designed Control Chartmentioning

confidence: 99%

“…In addition to the three-sigma limits, additional warning limits can be utilized. These are named the inner limits, usually placed at two-sigma (El-Din et al, 2006).…”

Section: Control Chart Limitsmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

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Industrial Robot Fault Detection Based on Statistical Control Chart

Jaber

Bicker

2016

American Journal of Engineering and Applied Sciences

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“…In addition to the three-sigma limits, additional warning limits can be utilized. These are named the inner limits, usually placed at two-sigma [30].…”

Section: Control Chart Limitsmentioning

confidence: 99%

“…A comparison of these features, to investigate which is the most faults sensitive one, was accomplished. The sensitive feature must be normally distributed, as this is a condition that has to be met in order to calculate the threshold values using SCC [28], [30]. The STD feature, which is normally used as a measure of extent of variation of the processed data and has the same units as the data, was found to be the most faults sensitive, and normally distributed one; and hence it was selected for comparison among fault severities.…”

Section: Robot Vibration Analysis and Feature Extractionmentioning

confidence: 99%

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

Jaber¹,

Bicker²

2017

IJECE

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Article history:Received Dec 7, 2016 Revised May 31, 2017 Accepted Jun 25, 2017 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.

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Directionally sensitive homogeneously weighted moving average control charts

Adegoke

Ganiyu²

2021

Quality & Reliability Eng

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One‐sided control charts for monitoring changes in the mean level are proposed in this paper. The proposed charts are given in the form of a homogeneously weighted moving average technique that provides efficient monitoring of small shifts in the mean level. The charts accumulate observations that are above the target (or mean value) and truncated the observations that are less than the target to the target value in their computations. Average run length comparisons of the proposed charts with the existing one‐sided charts, based on the exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, show that the proposed charts are more efficient in detecting small shifts than the competing charts. We investigate the sensitivity of the charts to non‐normality and show how they can be designed to be robust to non‐normal distributions. We provide a step‐by‐step implementation of the proposed charts when their parameters are unknown and estimated from historical reference data sets. The advantage of the proposed charts over some existing one‐sided charts is demonstrated via an illustrative example, involving monitoring mean lethal concentration (LC50) from a k‐nearest neighbours (KNN) regression‐based Quantitative Structure‐Activity Relationships (QSAR) model that relates LC50 to eight molecular descriptors.

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Statistical Process Control Charts Applied to Steelmaking Quality Improvement

Cited by 14 publications

References 1 publication

Industrial Robot Fault Detection Based on Statistical Control Chart

Industrial Robot Fault Detection Based on Statistical Control Chart

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

Directionally sensitive homogeneously weighted moving average control charts

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