Common in discrete manufacturing, timed event systems often have strict synchronization requirements for healthy operation. Discrete event system methods have been used as mathematical tools to detect known faults, but do not scale well for problems with extensive variability in the normal class. A hybridized discrete event and data-driven method is suggested to supplement fault diagnosis in the case where failure patterns are not known in advance. A unique fault diagnosis framework consisting of signal data from programmable logic controllers, a Timed Petri Net of the normal process behavior, and machine learning algorithms is presented to improve fault diagnosis of timed event systems. Various supervised and unsupervised machine learning algorithms are explored as the methodology is implemented to a case study in semiconductor manufacturing. State-of-the-art classifiers such as artificial neural networks, support vector machines, and random forests are implemented and compared for handling multi-fault diagnosis using programmable logic controller signal data. For unsupervised learning, classifiers based on principal component analysis utilizing major and minor principal components are compared for anomaly detection. The rule-based random forest and extreme random forest classifiers achieve excellent performance with a precision and recall score of 0.96 for multi-fault classification. Additionally, the unsupervised learning approach yields anomaly detection rates of 98% with false alarms under 3% with a training set 99% smaller than the supervised learning classifiers. These results obtained on a real use case are promising to enable prognostic tools in industrial automation systems in the future
Human factors practitioners in litigation sometimes base event reconstructions upon information from witnesses. The witnesses can be those who actively performed the at-issue tasks or those who merely observed the events. During this alternative-format interactive session, attendees will watch three event scenarios (two video and one role play). Attendees will then be queried about their recall of those scenarios, which will then be compared to the veridical information from the videos. The facilitators will help identify the particular eyewitness memory factors at play with the objectives of providing attendees (1) an experiential learning experience in which relevant eyewitness memory dynamics are demonstrated and discussed; (2) a summary of some of the cognitive-science literature concerning eyewitness memory for events and a framework to assist in evaluating and weighing the accuracy and completeness of eyewitness accounts; and (3) the general judicial posture toward expert testimony about eyewitness dynamics.
In the modern age of data collection in manufacturing industry, the sheer volume of measurement data collected may prove difficult for domain experts to create fully labeled training datasets for supervised learning artificial intelligence methods. Semi-supervised learning methods are useful in the realistic scenario where engineers may only be able to annotate limited partial subsets, but existing approaches are limited in scalability for high-dimensional and imbalanced datasets. To address these challenges, a novel framework for semisupervised learning is proposed that hybridizes 1) convolutional autoencoder as a deep unsupervised feature learning technique; 2) fault classification using principal component analysis-based anomaly scoring; and 3) fuzzy c-means clustering. Fuzzy c-means allows for transparency in the degree of membership to each cluster via the fuzzy partition matrix, enabling an adjustable, explainable, and computationally efficient approach for separating normal and faulty clusters in the same space. This also allows industry experts to review borderline cases, creating a support system to curtail costly labeling expenses. The approach is applied to real, high-dimensional data from a modern semiconductor manufacturing application in which fewer than 1000 out of over 59,000 samples are labeled, achieving AUC scores of over 0.94 for classifying the two labeled fault types as well as successful fuzzy clustering. These results show promise for deep, fuzzy semi-supervised applications to improve decision-making in manufacturing operations and other engineering disciplines.
Especially common in discrete manufacturing, timed event systems often require a high degree of synchronization for healthy operation. Discrete event system methods have been used as mathematical tools to detect known faults, but do not scale well for problems with extensive variability in the normal class. A hybridized discrete event and data-driven method is suggested to supplement fault diagnosis in the case where failure patterns are not known in advance. A unique fault diagnosis framework consisting of signal data from programmable logic controllers, a Timed Petri Net of the normal process behavior, and machine learning algorithms is presented to improve fault diagnosis of timed event systems. Various supervised and unsupervised machine learning algorithms are explored as the methodology is implemented to a case study in semiconductor manufacturing. State-of-the-art classifiers such as artificial neural networks, support vector machines, and random forests are implemented and compared for handling multi-fault diagnosis using programmable logic controller signal data. For unsupervised learning, classifiers based on principal component analysis utilizing major and minor principal components are compared for anomaly detection. The rule-based extreme random forest classifier achieves the highest validation accuracy of 98% for multi-fault classification. Likewise, the unsupervised learning approach shows similar success, yielding anomaly detection rates of 98% with false alarms under 3%. The industrial feasibility of this method is notable, with the results achieved with a training set 99% smaller than the supervised learning classifiers.
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