A review of machine learning approaches for high dimensional process monitoring (original) (raw)
Related papers
Intelligent data-driven monitoring of high dimensional multistage manufacturing processes
International Journal of Mechatronics and Manufacturing Systems, 2020
Recent advances in cyber-physical systems and the Internet of things (IoT) have enabled the possible development of smart production systems. However, the complexity of such a system has posed significant challenges for traditional quality engineering methods, especially in monitoring and diagnosis of system performance. The traditional practices for monitoring or controlling multistage systems either treat each stage as an individual entity or model all stages as a whole. The formal approach mainly focuses on the most critical stages while ignores information from the other stages. In contrast, the latter approach attempts to build one model to account for all stages. In a complex production system, this latter approach is impractical, if not impossible. This research provides a control strategy by proposing an intelligent process monitoring system for high dimensional multistage processes using predictive models built from historical data. A repository dataset is used to demonstrate the implementation of the proposed framework.
Neurocomputing, 2011
Statistical process control Support vector machine a b s t r a c t Advanced automatic data acquisition is now widely adopted in manufacturing industries and it is common to monitor several correlated quality variables simultaneously. Most of multivariate quality control charts are effective in detecting out-of-control signals based upon an overall statistics in multivariate manufacturing processes. The main problem of such charts is that they can detect an outof-control event but do not directly determine which variable or group of variables has caused the out-of-control signal and what is the magnitude of out of control. This study presents a hybrid learningbased model for on-line analysis of out-of-control signals in multivariate manufacturing processes. This model consists of two modules. In the first module using a support vector machine-classifier, type of unnatural pattern can be recognized. Then by using three neural networks for shift mean, trend and cycle it can be recognized magnitude of mean shift, slope of trend and cycle amplitude for each variable simultaneously in the second module. The performance of the proposed approach has been evaluated using two examples. The output generated by trained hybrid model is strongly correlated with the corresponding actual target value for each quality characteristic. The main contributions of this work are recognizing the type of unnatural pattern and classification major parameters for shift, trend and cycle and for each variable simultaneously by proposed hybrid model.
Machine learning techniques for quality control in high conformance manufacturing environment
Advances in Mechanical Engineering, 2018
In today's highly competitive global market, winning requires near-perfect quality. Although most mature organizations operate their processes at very low defects per million opportunities, customers expect completely defect-free products. Therefore, the prompt detection of rare quality events has become an issue of paramount importance and an opportunity for manufacturing companies to move quality standards forward. This article presents the learning process and pattern recognition strategy for a knowledge-based intelligent supervisory system, in which the main goal is the detection of rare quality events. Defect detection is formulated as a binary classification problem. The l 1-regularized logistic regression is used as the learning algorithm for the classification task and to select the features that contain the most relevant information about the quality of the process. The proposed strategy is supported by the novelty of a hybrid feature elimination algorithm and optimal classification threshold search algorithm. According to experimental results, 100% of defects can be detected effectively.
Supervised process monitoring and fault diagnosis based on machine learning methods
The International Journal of Advanced Manufacturing Technology, 2019
Data-driven techniques have been receiving considerable attention in the industrial process monitoring field due to their major advantages of easy implementation and less requirement for the prior knowledge and process mechanism. Principal component analysis (PCA) method is known as a popular method for monitoring and fault detection in industrial systems but as it is basically a linear method. However, most practical systems are nonlinear. To make the extension to nonlinear systems, kernel PCA (KPCA) method has been proposed for process modeling and monitoring. We present in this paper an online reduced rank optimized KPCA (RR-KPCA) technique for fault detection in order to extend the advantages of the KPCA models to online processes. Following the fault detection, the identification of the variables correlated to the fault occurred is of great importance. For this purpose, it is proposed to extend the approaches of localization by partial PCA and by elimination in the linear case to the nonlinear case, by exploiting the solution of reduction of the dimension of the kernel matrix in the feature space. The partial RR-KPCA and the elimination sensor identification (ESI-RRKPCA) are generated based on the static RR-KPCA and the online RR-KPCA methods. The idea of these approaches is to generate partial RR-KPCA models with reduced sets of variables. In other words, their goal is to generate indices of fault detection sensitive to certain faults and insensitive to others. The proposed fault isolation methods are applied for monitoring an air quality monitoring network (AIRLOR) data.
A Review on Basic Data-Driven Approaches for Industrial Process Monitoring
IEEE Transactions on Industrial Electronics, 2000
Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.
A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis
Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing
Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learning algorithms. Papers published from 2007 to 2017 were reviewed and keywords were used to identify 20 articles spanning the most prominent machine learning algorithms. Most articles reviewed consisted of training data obtained from sensors attached to the equipment. The training of the machine learning algorithm consisted of designed experiments to simulate different faulty and normal processing conditions. The areas of application varied from wear of cutting tool in computer numeric control (CNC) machine, surface roughness fault, to wafer etching process in semiconductor manufacturing. In all cases, high fault classification rates were obtained. As the interest in smart manufacturing increases, this review serves to address one of the cornerstones of emerging production systems.
Feature selection for high-dimensional industrial data
2005
In the semiconductor industry the number of circuits per chip is still drastically increasing. This fact and strong competition lead to the particular importance of quality control and quality assurance. As a result a vast amount of data is recorded during the fabrication process, which is very complex in structure and massively affected by noise. The evaluation of this data is a vital task to support engineers in the analysis of process problems. The current work tackles this problem by identifying the features responsible for success or failure in the manufacturing process (feature selection).
Development of a new machine learning-based informatics system for product health monitoring
53rd CIRP Conference on Manufacturing Systems, Chicago, IL, U.S., 2020
Manufacturing informatics aims to optimize productivity by extracting information from numerous data sources and making decisions based on that information about the process and the parts being produced. Manufacturing processes usually include a series of costly operations such as heat treatment, machining, and inspection to produce high-quality parts. However, performing costly operations when the product conformance to specifications cannot be achievable is not desirable. This paper develops a new machine learning-based informatics system capable of predicting the end product quality so that non-value-adding operations such as inspection can be minimized and the process can be stopped before completion when the part being manufactured fails to meet the design specifications.
2007
Statistical Process Control (SPC) Charts are one of several tools used in Quality Control. Other tools include flow charts, histograms, cause-and-effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process, signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division (ALDIJTI) has developed a hybrid machine-learninglexpert-system prototype which automates the process of constructing and interpreting control charts. INTRODUCTION The Air Force Logistics Command (AFLC) has provided TQM and Quality Control training to its employees for several years now. In particular, Statistical Process Control has been emphasized in this effort. While many data collection efforts have been undertaken within AFLC, the SPC Quality Control tool has b...
2020
Statistical Process Control (SPC) is a technique of gauging and monitoring quality by closely observing a given manufacturing process. Appropriate quality data is collected in the form of product measurements or readings from various machines. This data is used in evaluating, monitoring and controlling the variability of the considered manufacturing process. This paper proposes the expansion of SPC methods to predictive maintenance. Applications of SPC techniques in various fields outside of basic production systems have been increasing in popularity. This paper investigates the practicality and viability of using Control Charts in predictive maintenance and health monitoring. Moreover, this study discusses numerous enabling technologies, such as Industrial Internet of Things (IIOT), that help to advance real-time monitoring of industrial processes. This study also expands on the use of Naïve-Bayes and other Machine Learning methods to identify strong (naïve) dependencies between specific faults and special patterns in monitored measurements. Despite its idealistic independence assumption, the naïve Bayes classifier is effective in practice since its classification decision may often be correct even if its probability estimates are inaccurate. Optimal conditions of naïve Bayes will be also identified, and a deeper understanding of data characteristics that affect the performance of naïve Bayes is analyzed.