Manufacturing Process Monitoring With Nonparametric Change-Point Detection in Automotive Industry

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Shenghan Guo ◽  
Weihong (Grace) Guo ◽  
Amir Abolhassani ◽  
Rajeev Kalamdani ◽  
Saumuy Puchala ◽  
...  
Keyword(s):  
Change Point ◽  
Manufacturing Process ◽  
Manufacturing Industry ◽  
Systematic Approach ◽  
Decision Rules ◽  
Change Point Detection ◽  
Detection Methods ◽  
Automotive Manufacturing ◽  
Binary Segmentation ◽  
Point Detection

Automatic sensing devices and computer systems have been widely adopted by the automotive manufacturing industry, which are capable to record machine status and process parameters nonstop. While a manufacturing process always has natural variations, it is crucial to detect significant changes to the process for quality control, as such changes may be the early signs of machine faults. This motivates our study on change-point detection methods for automotive manufacturing. We aim at developing a systematic approach for detecting process changes retrospectively in complex, nonstationary data. The proposed approach consists of nonparametric change-point detection, alarm generation based on change-point estimations, and performance evaluation against historical maintenance records. For change-point detection, three nonparametric methods are suggested—least absolute shrinkage and selection operator (LASSO), thresholded LASSO, and wild binary segmentation (WBS). Multiple decision rules are proposed to determine how to generate alarms from change-point estimations. Numerical studies are conducted to demonstrate the performance of the proposed systematic approach. The different change-point detection methods and different decision rules are evaluated and compared, with scenarios for choosing one set of change-point detection method and decision rule over another combination identified. It is shown that LASSO and thresholded-LASSO outperform WBS when the shift size is small, but WBS produces a smaller false alarm rate and handles the clustering of changes better than LASSO or thresholded LASSO. Data from an automotive manufacturing plant are used in the case study to demonstrate the proposed approach. Guidelines for implementation are also provided.

scholarly journals Multi-Stage Change Point Detection with Copula Conditional Distribution with PCA and Functional PCA

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1777
Author(s):  
Jong-Min Kim ◽  
Ning Wang ◽  
Yumin Liu
Keyword(s):  
Change Point ◽  
Manufacturing Industry ◽  
Supply And Demand ◽  
Multivariate Data ◽  
Change Point Detection ◽  
Conditional Distributions ◽  
Multi Stage ◽  
Functional Pca ◽  
Point Detection ◽  
Uncertainty Environment

A global uncertainty environment, such as the COVID-19 pandemic, has affected the manufacturing industry severely in terms of supply and demand balancing. So, it is common that one stage statistical process control (SPC) chart affects the next-stage SPC chart. It is our research objective to consider a conditional case for the multi-stage multivariate change point detection (CPD) model for highly correlated multivariate data via copula conditional distributions with principal component analysis (PCA) and functional PCA (FPCA). First of all, we review the current available multivariate CPD models, which are the energy test-based control chart (ETCC) and the nonparametric multivariate change point model (NPMVCP). We extend the current available CPD models to the conditional multi-stage multivariate CPD model via copula conditional distributions with PCA for linear normal multivariate data and FPCA for nonlinear non-normal multivariate data.

scholarly journals Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models

2020 ◽  
Author(s):  
Simon Letzgus
Keyword(s):  
Wind Turbine ◽  
Change Point ◽  
Significant Loss ◽  
Change Point Detection ◽  
Training Data ◽  
Change Points ◽  
Detection Methods ◽  
Full Potential ◽  
Normal Behaviour ◽  
Point Detection

Abstract. Analysis of data from wind turbine supervisory control and data acquisition (SCADA) systems has attracted considerable research interest in recent years. The data is predominantly used to gain insights into turbine condition without the need for additional sensing equipment. Most successful approaches apply semi-supervised anomaly detection methods, also called normal behaivour models, that use clean training data sets to establish healthy component baseline models. However, one of the major challenges when working with wind turbine SCADA data in practice is the presence of systematic changes in signal behaviour induced by malfunctions or maintenance actions. Even though this problem is well described in literature it has not been systematically addressed so far. This contribution is the first to comprehensively analyse the presence of change-points in wind turbine SCADA signals and introduce an algorithm for their automated detection. 600 signals from 33 turbines are analysed over an operational period of more than two years. During this time one third of the signals are affected by change-points. Kernel change-point detection methods have shown promising results in similar settings but their performance strongly depends on the choice of several hyperparameters. This contribution presents a comprehensive comparison between different kernels as well as kernel-bandwidth and regularisation-penalty selection heuristics. Moreover, an appropriate data pre-processing procedure is introduced. The results show that the combination of Laplace kernels with a newly introduced bandwidth and penalty selection heuristic robustly outperforms existing methods. In a signal validation setting more than 90 % of the signals were classified correctly regarding the presence or absence of change-points, resulting in a F1-score of 0.86. For a change-point-free sequence selection the most severe 60 % of all CPs could be automatically removed with a precision of more than 0.96 and therefore without a significant loss of training data. These results indicate that the algorithm can be a meaningful step towards automated SCADA data pre-processing which is key for data driven methods to reach their full potential. The algorithm is open source and its implementation in Python publicly available.

scholarly journals Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data

Smart Cities ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 1-16
Author(s):  
Haoran Niu ◽  
Olufemi A. Omitaomu ◽  
Qing C. Cao
Keyword(s):  
Event Detection ◽  
Change Point ◽  
Power Grid ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Disturbance Analysis ◽  
Events Detection ◽  
Point Detection

Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.

DEVELOPING TIME-BASED CLUSTERING NEURAL NETWORKS TO USE CHANGE-POINT DETECTION: APPLICATION TO FINANCIAL TIME SERIES

2005 ◽  
Vol 22 (01) ◽  
pp. 51-70 ◽  
Author(s):  
KYONG JOO OH ◽  
TAE HYUP ROH ◽  
MYUNG SANG MOON
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Change Point ◽  
Financial Time Series ◽  
Change Point Detection ◽  
Detection Methods ◽  
Financial Time ◽  
Point Groups ◽  
Point Detection

This study suggests time-based clustering models integrating change-point detection and neural networks, and applies them to financial time series forecasting. The basic concept of the proposed models is to obtain intervals divided by change points, to identify them as change-point groups, and to involve them in the forecasting model. The proposed models consist of two stages. The first stage, the clustering neural network modeling stage, is to detect successive change points in the dataset, and to forecast change-point groups with backpropagation neural networks (BPNs). In this stage, three change-point detection methods are applied and compared. They are: (1) the parametric approach, (2) the nonparametric approach, and (3) the model-based approach. The next stage is to forecast the final output with BPNs. Through the application to financial time series forecasting, we compare the proposed models with a neural network model alone and, in addition, determine which of three change-point detection methods performs better. Furthermore, we evaluate whether the proposed models play a role in clustering to reflect the time. Finally, this study examines the predictability of the integrated neural network models based on change-point detection.

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