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A machine vision approach for detecting changes in drill core textures using optical images

MINING TECHNOLOGY-TRANSACTIONS OF THE INSTITUTIONS OF MINING AND METALLURGY 2024年第2期133卷 162-175页

作者： Gu, Xiaomeng Cook, Nigel J. Metcalfe, Andrew V. Aldrich, Chris Univ Adelaide Sch Chem Engn Adelaide SA 5005 Australia Univ Adelaide Sch Comp & Math Sci Adelaide Australia Curtin Univ Western Australian Sch Mines Minerals Energy & Che Perth Australia

Drill core images offer valuable insights into the texture, structure and mineralogy of ores and their host rocks, which can be used to optimise downstream processes in the mining industry. The impact on downstream processes from particles of similar composition and mineralogy but different textures has been examined by several previous researchers through the application of supervised machine-learning techniques. This study proposes a novel approach for detecting changes in drill core textures through the analysis of optical images. This approach compares three widely used image feature extraction techniques (local binary patterns, grey-level co-occurrence matrix and convolutional neural network), followed by calculation of a uniqueness measure, based on the Hotelling statistic, designed to identify anomalous segments of core. The effectiveness of the uniqueness measure is validated on a test core comprising six sections with different textures. Two drill cores, from the Brukunga test site in South Australia, were selected as case studies. Of the three feature extraction methods, local binary patterns were found to give the strongest signals of change. There exist two main regimes that separate halfway along both drill cores, indicating a change in lithology or the presence of mineralisation.

关键词： texture analysis change detection multivariate statistical process control mineral processing uniqueness measure

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One-Class Classification-Based Monitoring of Mineral processing Operations with Hyperensemble Tree-Models

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IFAC-PapersOnLine 2024年第22期58卷 95-100页

作者： Chris Aldrich Xiu Liu Western Australian School of Mines Curtin University WA Australia 8030

Reliable monitoring of mineral process systems is key to more efficient plant operation. multivariate statistical process control based on principal component analysis is well-established in industry, but may not be effective when dealing with nonlinear or transient processes, where process behaviour can change rapidly over time. Although many advances have been made in unsupervised process monitoring, the detection of incipient process drift or nascent abnormal process conditions remains an open research problem. In this investigation, monitoring of mineral process operations based on the use of random forest classifiers is considered. When the random forests are used, data representing normal operating conditions are split into subclasses with K-means clustering, after which an ensemble of random forests is trained as binary classifiers on the subclasses in a one-against-the-rest approach. These hyperensemble models are subsequently used to classify new operational data as in-control or out-of-control. One simulated and one real world case study are considered. In each case, the models were trained on data representing normal operating conditions and then tested on new process data that were generally different from the training data to test their ability to identify these data as out-of-control. In the real-world case study, the approach is compared with traditional multivariate statistical process monitoring based on a principal component model, as well as a recently proposed isolation forest model. The random forest-based models performed markedly better than the principal component model and slightly better than the isolation forest model as far as identification of out-of-control data were concerned, but this came at the expensive of a significantly larger false positive rate than these two models.

关键词： mineral processing semi-autogenous grinding one-class classification intra-class splitting random forest isolation forest multivariate statistical process control principal component analysis

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Integrating PCA and structural model decomposition to improve fault monitoring and diagnosis with varying operation points

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 122卷

作者： Garcia-Alvarez, D. Bregon, A. Pulido, B. Alonso-Gonzalez, C. J. Univ Valladolid Dept Informat Valladolid 47011 Spain

Fast and efficient fault monitoring and diagnostics methods are essential for fault diagnosis and prognosis tasks in Health Monitoring Systems. These tasks are even more complicated when facing dynamic systems with multiple operation points. This article introduces a symbiotic solution for fault detection and isolation, based on the integration of two complementary techniques: Possible Conflicts (PCs), a model-based diagnosis technique from the Artificial Intelligence (AI) community, and Principal Component Analysis (PCA), a multivariate statistical process control (MSPC) technique. Our proposal improves the PCA-based fault detection in systems with multiple operation points and transient states and provides a straightforward fault isolation stage for PCA. At the same time, the proposal increases the robustness for fault detection using PCs through the application of PCA to the residual signals. PCA has the ability to filter out residual deviations caused by model uncertainties that can lead to a high number of false positives. The proposed method has been successfully tested in a real-world plant with accurate fault detection results. The plant has noisy sensors and a system model without the same accuracy at each operation point and transient states.

关键词： Fault diagnosis Model decomposition Possible Conflicts multivariate statistical process control Principal Component Analysis Multiple Operation Points Systems

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Real-time monitoring and fault detection of pulsed-spray fluid-bed granulation using near-infrared spectroscopy and multivariate process trajectories

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Particuology 2020年第6期18卷 112-123页

作者： Jie Zhao Wenlong Li Haibin Qu Geng Tian Yanding Wei Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province College of Mechanical EngineeringZhejiang UniversityHangzhou 310027China

Pulsed spray is a useful tool forgranule size control in fluid bed *** improve the quality control of pulsed-spray fluid bed granulation,a combination of in-line near-infrared(NIR)spectroscopy and p「incipal component analysis was used to develop multivariate statistical process control(MSPC)*** types of MSPC charts were developed,including principal component score charts,Hotelling's T2 control charts,and distance to model X control charts,to monitor the batch evolution throughout the granulation *** optimized warping was used as an alignment method to deal with the time variation in batches caused by the granulation mechanism in MSPC *** control charts developed in this study were validated on normal batches and tested on four batches that deviated from normal processing conditions to achieve real-time fault *** results indicated that the NIR spectroscopy-based MSPC model included the variability in the sample set constituting the model and could withstand external *** research demonstrated the application of synchronized NIR spectra in conjunction w让h multivariate batch modeling as an attractive tool for process monitoring and a fault diagnosis method for effective process control in pulsed-spray fluid bed granulation.

关键词： Pulsed-spray fluid bed granulation multivariate statistical process control Multiway principal component analysis Near-infrared spectroscopy Correlation optimized warping

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Study on robust model construction method of multi-batch fruit online sorting by near-infrared spectroscopy

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022年 280卷 121478页

作者： Hao, Yong Lu, Yuanhang Li, Xiyan East China Jiaotong Univ Sch Mechatron & Vehicle Engn Nanchang 330013 Peoples R China

In the online detection of fruit samples by near-infrared spectroscopy (NIRS), the natural change of sample states or the variations of instruments will often cause a large error in predicting different batches of samples. In this study, a total of 440 tomato samples were collected in four batches with each batch of 110 samples. The Spectral and soluble solids content (SSC) of single batch were collected every other day in batch order. The multivariate statistical process control (MSPC) method was adopted to establish a stability monitor model. The robustness regression (Rob-Reg) and partial least squares regression (PLSR) were used for mixed modeling of multiple batches of samples to eliminate the variability influence of sample and instrument states. The results show that MSPC can effectively monitor the consistency of the same batch samples measured at different times or different batches. The variation of sample attributes with spectral acquisition time has dramatically damaged the adaptation of PLSR models. The Rob-Reg method can predict the SSC of the different batches of samples at different collection times. Compared with the PLSR method, the correlation coefficient of prediction (Rp) was improved from 0.61 to 0.66, and the root mean square error of prediction (RMSEP) was decreased from 0.55 to 0.44 for Rob-Reg method. The RPD of 3.85 indicated that the model is excellent. The Robust modeling method can be well applied to fruit near-infrared online detection system.

关键词： Near-infrared spectroscopy multivariate statistical process control Robustness regression Partial least squares regression

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A Multiblock Approach to Fuse process and Near-Infrared Sensors for On-Line Prediction of Polymer Properties

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SENSORS 2022年第4期22卷 1436-1436页

作者： Strani, Lorenzo Vitale, Raffaele Tanzilli, Daniele Bonacini, Francesco Perolo, Andrea Mantovani, Erik Ferrando, Angelo Cocchi, Marina Univ Modena & Reggio Emilia Dept Chem & Geol Sci Via 4 Campi 103 I-41125 Modena Italy Univ Lille Lab Spect Interact React & Environm Ctr Natl Rech Sci CNRS F-59000 Lille France Versalis ENI SpA Res Ctr Via Taliercio 14 I-46100 Mantua Italy

Petrochemical companies aim at assessing final product quality in real time, in order to rapidly deal with possible plant faults and to reduce chemical wastes and staff effort resulting from the many laboratory analyses performed every day. In order to answer these needs, the main purpose of the current work is to explore the feasibility of multiblock regression methods to build real-time monitoring models for the prediction of two quality properties of Acrylonitrile-Butadiene-Styrene (ABS) by fusing near-infrared (NIR) and process sensors data. Data come from a production plant, which operates continuously, and where four NIR probes are installed on-line, in addition to standard process sensors. Multiblock-PLS (MB-PLS) and Response-Oriented Sequential Alternation (ROSA) methods were here utilized to assess which of such sensors and plant areas were the most relevant for the quality parameters prediction. Several prediction models were constructed exploiting measurements provided by sensors active at different ABS production process stages. Both methods provided good prediction performances and permitted identification of the most relevant data blocks for the quality parameters' prediction. Moreover, models built without considering recordings from the final stage of the process yielded prediction errors comparable to those involving all available data blocks. Thus, in principle, allowing final ABS quality to be estimated in real-time before the end of the process itself.

关键词： Acrylonitrile-Butadiene-Styrene low-level data fusion multiblock-partial least squares (MB-PLS) multivariate statistical process control polymer production quality prediction real-time monitoring response-oriented sequential alternation (ROSA)

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process Fault Diagnosis Method Based on MSPC and LiNGAM and its Application to Tennessee Eastman process

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IFAC-PapersOnLine 2022年第2期55卷 384-389页

作者： Yoshiaki Uchida Koichi Fujiwara Tatsuki Saito Taketsugu Osaka Dept. of Material Process Engineering Nagoya University Nagoya Japan 464-8601 Kobe Corporate Research Laboratories Kobe Steel Kobe Hyogo Japan 651-2271

This paper proposes a new fault diagnosis method that combines multivariate statistical process control (MSPC) and a linear non-gaussian acyclic model (LiNGAM), referred to as MSPC-LiNGAM. MSPC is a widely adopted process monitoring method based on principal component analysis (PCA). In MSPC, T 2 and Q statistics are used as monitoring indexes for fault detection. Contribution plots based on T 2 and Q statistics have been proposed for fault diagnosis. However, contribution plots do not always appropriately diagnose causes of faults. In this study, a new fault diagnosis method based on MSPC and a Linear Non-Gaussian Acyclic Model (LiNGAM) is proposed. In the proposed method, referred to as MSPC-LiNGAM, the causality among the T 2 or Q statistic in addition to process variables is calculated by LiNGAM without prior knowledge of processes, and process variables that have the strength of causality to the T 2 or Q statistic are identified as candidates of the causes of the fault. The proposed MSPC-LiNGAM was applied to a simulation data of the Tennessee Eastman (TE) process. The result showed that the proposed method appropriately diagnosed faults even when the conventional contribution plots did not correctly identify causes of faults.

关键词： Fault detection diagnosis multivariate statistical process control Linear non-Gaussian acyclic model Causal inference Contribution plot

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Improved neural component analysis for monitoring nonlinear and Non-Gaussian processes

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MEASUREMENT 2022年 195卷

作者： Lou, Zhijiang Li, Zedong Wang, Youqing Lu, Shan Shenzhen Polytech Inst Inlligence Sci & Engn Shenzhen 518055 Peoples R China Qingdao Agr Univ Coll Mech & Elect Engn Qingdao 266109 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

Neural component analysis (NCA) is one of the latest nonlinear multivariate statistical process control (MSPC) methods, which consists in combining artificial neural networks (ANNs) with principal component analysis (PCA). However, NCA cannot handle the non-Gaussian feature and the extracted principal components (PCs) in NCA may not be the key information in the process data. Herein, we propose an improved NCA (INCA) which introduces a new cost function based on kurtosis to restrict the Gaussianity of PCs. We also propose a novel PC selection mechanism based on the information of PCs in the original data space rather than in the PC data space. INCA achieves almost 100% detection rates in three different types of faults in a simulation model test, and it can detect the fault in the thermal power plant process more than 1 min ahead of orthogonal nonlinear PCA (ONLPCA) and NCA.

关键词： Improved neural component analysis multivariate statistical process control Principal component analysis

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Bivariate semiparametric control charts for simultaneous monitoring of process mean and variance

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2020年第2期36卷 447-473页

作者： Koutras, Markos, V Sofikitou, Elisavet M. Univ Piraeus Sch Finance & Stat Dept Stat & Insurance Sci Piraeus Greece

In the present article, two semiparametric bivariate control charts are presented, which use order statistics and are effective in jointly monitoring of possible shifts in the process mean and/or variance. To achieve that both the median location (or more generally the location of a specific order statistic) and the number of specific observations of the test sample lying between the control limits are taken into account. The false alarm rate and the in-control average run length are not affected by the marginal distributions, while the effect of the dependence structure on them is negligible;therefore, they can be used as fully nonparametric charts. A performance-comparison study is carried out, and an illustrative example is provided using a real-world data set.

关键词： copulas multivariate statistical process control nonparametric control charts order statistics simultaneous monitoring

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A Monitoring Technique Using multivariate statistical process control Method for Performance Improvement with Application to Wastewater Treatment Plant Operation

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IFAC Proceedings Volumes 2012年第16期45卷 619-624页

作者： O. Yamanaka N. Yoshizawa Y. Hiraoka K. Sano T. Hamada Toshiba Corporation Tokyo Japan Japan Sewage Works Agency Tokyo Japan

This paper presents a new monitoring technique to improve the performance of plant operation based on multivariate statistical process control (MSPC). The proposed method combines with MSPC by principal component analysis (PCA-MSPC) and monitoring of a predefined performance index for efficient and stable plant operation. Fault detection and isolation (FDI) related to the performance index is selectively performed by monitoring the time series data of the index wherein the sample points violating the control limit of the Q statistic or that of the T 2 statistic in PCA-MSPC are indicated. Hidden patterns of probable cause to deteriorate the performance index are discovered from the FDI by observing the time series data of the isolated variables. Applications of the proposed method to real wastewater treatment process illustrate the effectiveness of the proposed method by showing possible improvement for energy-saving operation and stable plant operation.

关键词： multivariate statistical process control Monitoring and Fault Detection Energy Saving, Performance Improvement Wastewater Treatment process Principal Component Analysis

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