Process Fault Research Articles

A Novel Distributed Process Monitoring Framework of VAE-Enhanced with Deep Neural Network

Intelligent manufacturing process needs to adopt distributed monitoring scenario due to its massive, high-dimensional and complex data. Distributed process monitoring has been introduced into global monitoring and local monitoring to analyze the characteristic relationship between process data. However, the existing framework methods ignore or suppress the fault information and thus cannot effectively identify the local faults and the time sequence characteristics between units in the batch production system. This paper proposes a novel distributed process monitoring framework based on Girvan-Newman algorithm modular subunit partitioning and probabilistic learning model with deep neural networks. First, Girvan-Newman algorithm is used to divide the complex manufacturing system modularized to reduce the latitude of data processing. Second, variational autoencoder (VAE) is adopted to ensure the stability of local analysis, and long short-term memory is adopted to improve the VAE model to detect global multi-time scale anomalies. Finally, distributed process fault detection is carried out for each subunit in a separate and integrated manner, and the performance of the framework in distributed process monitoring is analyzed through two fault detection indicators T2 and SPE statistics. A case study of the Tennessee Eastman Process is used to demonstrate the performance and applicability of the proposed framework. Results show that the proposed VAE enhancement framework based on the DNN could accurately identify faults in distributed process monitoring and locate the specific sub-units where the fault occurs. Compared with VAE-DNN method and traditional process monitoring methods, the framework proposed in this paper has higher fault detection rate and lower false alarm rate, and the detection rate of some faults can reach 100%.

Domain generalization of chemical process fault diagnosis by maximizing domain feature distribution alignment

Fault detection and diagnosis (FDD) is crucial for ensuring safety and quality control in chemical industry. But data-driven FDD methods are usually trained with samples under certain operating conditions or data domains. This leads to their performance degradation caused by domain shifts in practice. To address this, a new FDD network, termed the DFDA model, has been designed to achieve domain generalization. Distribution of features extracted from fault samples is progressively aligned by supervised contrastive learning, learning vector quantization and gradient reversal adversarial learning in the DFDA model. So that FDD using such aligned features can potentially offer enhanced domain generalization capabilities. The performance of the DFDA model is benchmarked against other state-of-the-art FDD frameworks using data from various domains/modes of the Tennessee Eastman Process and an actual industrial process. The experimental outcomes indicate that the DFDA model consistently outperforms others in five distinct tasks, achieving an average Fault Detection Rate (FDR) that is 23% higher than that of ViT and 16% higher than CausalViT.

Fault diagnosis with high accuracy and timeliness for semi‐batch crystallization process based on deep learning with multiple pattern representation

AbstractThe research on chemical process fault diagnosis has made significant progress, but there is still a big gap in its application to complex practical industrial processes. As for the fault diagnosis of batch crystallization processes, the recently‐proposed dynamic time warping–convolutional neural network (DTW‐CNN) model has achieved a great improvement in the fault diagnosis. However, its fault diagnosis rate (FDR) and timeliness of fault diagnosis are still low, and thus, it needs to improve further before being applied to the practical application. In this paper, a multiple pattern representation–convolutional neural network (MPR‐CNN) model is proposed and applied for the fault diagnosis of a semi‐batch crystallization process. The MPR‐CNN model enables the manual extraction of features with four pattern representation algorithms in the data pre‐processing stage, and generates a three‐dimensional matrix which is used as the training sample and input to the CNN for the formal feature extraction and weight learning. An excellent classification performance, with an average FDR of 97.5%, is achieved. This model is also applied for the fault diagnosis of process data within a shorter period of time after the occurrence of faults. The results indicate that the model could make timely fault diagnosis with a highly stable and accurate performance after the occurrence of a fault.

Monitoring dynamic process with orthonormal subspace analysis

AbstractTraditional multivariate statistics‐based process monitoring (MSPM) methods are static algorithms, and the “time lag shift” method (TLSM) is the most commonly used approach to handle the dynamic issue. This paper proves in theory that two drawbacks exist in TLSM‐based dynamic approaches: information unrelated to the real‐time data is also analyzed, and information that can be predicted by historical data is counted repeatedly in both real‐time and historical data. This paper adopts orthonormal subspace analysis (OSA) to handle these issues. OSA can successfully separate real‐time data into information that can be predicted by historical data (the dynamic component) and cannot be predicted for process monitoring (the static component), so the detection result is not influenced by redundant information and is more sensitive to process faults than TLSM‐based dynamic methods.

Variable contribution analysis in multivariate process monitoring using permutation entropy

Multivariate statistical process monitoring is widely used in industrial processes for performance monitoring and fault detection. Once a fault has been detected, fault diagnoses must be performed to identify the variables that are responsible for the fault or the deviation from normal operation. In this paper, we present a new data-driven method for variable contribution analysis to a fault or abnormal behaviour in a process. The method is based on permutation entropy (PE), which measures the order of fluctuations within a time series. The PE method for variable contribution analysis is independent of fault detection statistics, and is used to evaluate which variables experienced the greatest change in their expected behaviour over time, immediately after the fault has been identified. We show how PE can be used to identify and interpret the variables responsible for, or affected by faults in an industrial process. The well-known Tennessee Eastman Process (TEP) is used to illustrate the application of PE for variable contribution analysis. The results show that PE is an efficient analysis tool for specifying variable contributions to faults.

Steps to improve the teaching of clinical reasoning in dermatology: A scoping review and proposal.

Clinical reasoning (CR) is an area of active interest since faults in the diagnostic process can result in errors and possibly delays in care or even patient harm. The purpose of this scoping review was to collect information from the medical literature on approaches utilized to teach and assess CR in dermatology, identify gaps, and prepare a proposal on how to enhance the speciality's ability to develop trainee CR skills. We conducted a review of the published literature (1990-2020) from four databases. The initial search yielded 780 papers, and 42 relevant CR publications met inclusion criteria. Demographic, thematic content, theoretical frameworks, continuum of authenticity, competency/milestone, and assessment/educational intervention data were recorded by two screeners. Trainees at different educational levels from 17 different countries have been assessed in the dermatology literature. Most publications were of a single intervention, appeared underpowered, and had small sample sizes. Only two publications examined work-based assessments (use real patients/stimuli). Knowledge-based studies were the dominant theoretical framework with no studies exclusively focused on process-based CR interventions. Simulation was well represented with 23 (55%) investigations. Rigorous studies that examine CR teaching and assessment in dermatology are lacking. Evidence-based best practices for use of work-based assessments, especially direct observation, need to be developed/adapted for dermatology and validated. Dermatology training programs would benefit from longitudinal data on trainee CR development, process-based CR educational programs, metacognition CR exploration specific to skin disease diagnosis, and studies that yield practical recommendations on how to structure multi-faceted assessments that assess CR.

Application of Multivariate Data Analysis on Historical Recombinant Adenovirus Zoster Vaccine Production Data for Upstream Process Improvements

In recent years, multivariate data analysis (MVDA) has been widely used for process characterization and fault diagnosis in the biopharmaceutical industry. This study aims to investigate the feasibility of using MVDA for the development and scale-up of a perfusion process for HEK293 cell-based recombinant adenovirus zoster vaccine (Ad-HER) production. The Principal Component Analysis (PCA) results suggested comparable performance among the ATF, PATFP, and BFP perfusion systems in benchtop-scale stirred-tank bioreactor (STR). Then a Batch Evolution Model (BEM) was built using representative data from 10 L STR with a BFP system to assess the Ad-HER perfusion process performance at pilot-scale bioreactor (50 L STR and 50 L wave bioreactor). Furthermore, another BEM model and Batch Level Model (BLM) were built to monitor process parameters over time and predict the final adenovirus titer in 50 L wave bioreactor. The loading plot revealed that lactate dehydrogenase activity, viable cell diameter, and base-added during the virus production phase could be used as preliminary indicators of adenovirus yield. Finally, an adenovirus titer of 2.0±0.3×1010 IFU/mL was achieved in the 50 L wave bioreactor with BFP system, highlighting the robustness of the Ad-HER perfusion process at pilot-scale. Overall, this study emphasizes the effectiveness of MVDA as a tool for advancing the understanding of recombinant adenovirus vaccine perfusion production process development and scale-up.

Tapping process fault identification by LSTM neural network based on torque signal singularity feature

Tapping process fault identification by LSTM neural network based on torque signal singularity feature

Process monitoring and fault diagnosis method combining bagging DPCA‐ICA with moving window Kolmogorov–Smirnov test

AbstractProcess monitoring and fault diagnosis systems are essential for ensuring the quality and safety of industrial processes. Real industrial process data always has dynamic features, autocorrelation features, and non‐Gaussian distributions, making many multivariate statistical process monitoring (MSPM) methods, such as principal component analysis (PCA), not work well. To solve the problem, a new process monitoring method (named DPCA‐ICA‐moving window Kolmogorov–Smirnov test [MWKS]) is proposed in the paper, which is a combination of DPCA and independent component analysis (ICA) with adaptive weight parameters and introduces a moving window technique and two‐sample K‐S test to obtain the K‐S distance of the commonly used Hotelling's and square predictive error as new monitoring parameters. Experimental results on the Tennessee Eastman chemical process demonstrate the effectiveness and accuracy of the proposed method, which shows it has better performance than existing commonly used methods. The average fault detection rate (FDR) is 93.131%, while the average false alarm rate (FAR) of the proposed method is only 0.1%.

Dual-attention LSTM autoencoder for fault detection in industrial complex dynamic processes

Complex dynamic characteristics resulting from multi-system coupling and closed-loop control are ubiquitous in modern industrial process data, presenting significant challenges for process fault detection. However, conventional data-driven fault detection methods assume the data to be static or slightly dynamic. Addressing the complex dynamic characteristics and nonlinearity inherent in industrial processes, this paper proposes a dual-attention long short-term memory autoencoder (DALSTM-AE) for fault detection in dynamic processes. Long short-term memory (LSTM) and autoencoder (AE) are combined into a special encoder-decoder LSTM architecture to learn both dynamic features and deep representations of variables in an unsupervised manner. Then, a dual-attention module is embedded in the decoder to properly learn the temporal dependencies associated with long input sequences and retain the most critical information. In addition, based on the reconstruction results of the DALSTM-AE model, two monitoring statistics are designed for fault detection. Finally, the effectiveness and superiority of the proposed method are fully demonstrated through case studies on a numerical simulation example, the Tennessee Eastman (TE) benchmark process, and practical coal pulverizing systems in power plants.

Gap-MK-DCCA-Based Intelligent Fault Diagnosis for Nonlinear Dynamic Systems

In intelligent process monitoring and fault detection of the modern process industry, conventional methods mostly consider singular characteristics of systems. To tackle the problem of suboptimal incipient fault detection in nonlinear dynamic systems with non-Gaussian distributed data, this paper proposes a methodology named Gap-Mixed Kernel-Dynamic Canonical Correlation Analysis. Initially, the Gap metric is employed for data preprocessing, followed by fault detection utilizing the Mixed Kernel-Dynamic Canonical Correlation Analysis. Ultimately, fault identification is conducted through a contribution method based on the T2 statistic. Furthermore, a comparative analysis was conducted using Canonical Variate Analysis, Dynamic Canonical Correlation Analysis, and Mixed Kernel-Dynamic Canonical Correlation Analysis on the Tennessee Eastman process. Experimental results indicate varying degrees of improvements in the detection rate, false alarm rate, missed detection rate, and detection time compared to the comparative methods, demonstrating the industrial value and academic significance of the method.

Industrial process fault diagnosis based on feature enhanced meta-learning toward domain generalization scenarios

With the rise of the Internet of Things, computing science, and artificial intelligence, data-driven fault diagnosis methods for industrial processes have attracted considerable attention. However, these methods typically assume that the distribution of the test data (target domains) is similar to that of the training data (source domains), making it difficult to deploy them in actual scenarios because the distribution of the collected data varies with operating conditions. Faced with this challenge, a novel fault diagnosis method based on feature enhanced meta-learning (FEML) is proposed to learn domain generalization knowledge effectively. It aims to jointly extract domain-invariant information that can be transferred across domains, and domain-specific information related to classification labels. First, feature extractors based on gated recurrent units (GRUs) are designed to capture domain-invariant and domain-specific representations with time-sequence characteristics from industrial process data. Second, a meta-learning strategy is introduced to enhance the domain-specific representation, thus improving the generalization ability of the model without accessing the target domains. Finally, the Tennessee Eastman process (TEP), Fed-Batch Fermentation Penicillin process (FBFP), and Three-Phase Flow process (TPFP) are used to verify the effectiveness of FEML. The results illustrate that the FEML is robust and outperforms other related methods. The diagnostic accuracies for TEP, FBFP, and TPFP are 87.11%, 95.94%, and 83.07%, respectively.

Explainable artificial intelligence-based evidential inferencing on process faults in plasma etching

The fault detection and classification (FDC) modeling proposed in this study is a research approach that is intended to improve the performance of plasma process models by leveraging optical emission spectroscopy (OES) data containing plasma information (PI) and enhancing model interpretability using explainable artificial intelligence (XAI) algorithms. Status variable identification data that included normal and abnormal states of bias power, pressure, SF6 gas flow, and O2 gas flow were collected during a silicon etching process with SF6, O2 gas plasma. Additional variables were derived from the OES data and included additional PI, such as O and F radicals, which were computed using actinometry, and electron temperature and electron density computed using the line ratio method. By building a high-performance FDC model and interpreting its results using XAI algorithms, we propose solutions to the limitations of the FDC model in semiconductor plasma processes.

Accurate Liquid Level Measurement with Minimal Error: A Chaotic Observer Approach

This paper delves into precisely measuring liquid levels using a specific methodology with diverse real-world applications such as process optimization, quality control, fault detection and diagnosis, etc. It demonstrates the process of liquid level measurement by employing a chaotic observer, which senses multiple variables within a system. A three-dimensional computational fluid dynamics (CFD) model is meticulously created using ANSYS to explore the laminar flow characteristics of liquids comprehensively. The methodology integrates the system identification technique to formulate a third-order state–space model that characterizes the system. Based on this mathematical model, we develop estimators inspired by Lorenz and Rossler’s principles to gauge the liquid level under specified liquid temperature, density, inlet velocity, and sensor placement conditions. The estimated results are compared with those of an artificial neural network (ANN) model. These ANN models learn and adapt to the patterns and features in data and catch non-linear relationships between input and output variables. The accuracy and error minimization of the developed model are confirmed through a thorough validation process. Experimental setups are employed to ensure the reliability and precision of the estimation results, thereby underscoring the robustness of our liquid-level measurement methodology. In summary, this study helps to estimate unmeasured states using the available measurements, which is essential for understanding and controlling the behavior of a system. It helps improve the performance and robustness of control systems, enhance fault detection capabilities, and contribute to dynamic systems’ overall efficiency and reliability.

Industrial Process Monitoring Based on Dynamic Overcomplete Broad Learning Network.

Most industrial processes feature high nonlinearity, non-Gaussianity, and time correlation. Models based on overcomplete broad learning system (OBLS) have been successfully applied in the fault monitoring realm, which may relatively deal with the nonlinear and non-Gaussian characteristics. However, these models barely take time correlation into full consideration, hindering the further improvement of the monitoring accuracy of the network. Therefore, an effective dynamic overcomplete broad learning system (DOBLS) based on matrix extension is proposed, which extends the raw data in the batch process with the idea of "time lag" in this article. Subsequently, the OBLS monitoring network is employed to continue the analysis of the extended dynamic input data. Finally, a monitoring model is established to tackle the coexistence of nonlinearity, non-Gaussianity, and time correlation in process data. To illustrate the superiority and feasibility, the proposed model is conducted on the penicillin fermentation simulation platform, the experimental result of which illustrates that the model can extract the feature of process data more comprehensively and be self-updated more efficiently. With shorter training time and higher monitoring accuracy, the proposed model can witness an improvement of average monitoring accuracy by 3.69% and 1.26% in 26 process fault types compared to the state-of-the-art fault monitoring methods BLS and OBLS, respectively.

A Data Driven Machine Learning Model for Fault Detection in Industrial Pipelines

Industry 4.0 has marked a paradigm shift in the way production and manufacturing operates. Process monitoring and fault diagnosis are important for the safety and reliability of industrial processes especially for smart manufacturing. As a data-driven process monitoring methodology, multivariate statistical analysis techniques, such machine learning based approaches have become extremely critical for automation. Pipelines carrying oil and gas are essential for a nation's economic sustainability. In order to maximise their function and prevent product losses during the transportation of petroleum products, they must be carefully inspected. However, they are susceptible to failure, which could have negative effects on the environment, financial loss, and safety. Therefore, evaluating the pipe's state and quality would be crucial. Despite being time-consuming and expensive, a number of inspection procedures are used to assure the safety of pipelines. However, due to the time consumption and error prone nature of manual inspection, data driven models are being explored for forecasting failure in oil and gas pipelines. The proposed work presents a Bayesian Regularized classifier for forecasting failure and the results show that the proposed approach outperforms the existing baseline techniques in terms of forecasting accuracy. Keywords:- Industry 4.0, oil pipelines; failure forecasting, Bayesian Regularization, Confusion Matrix, Classification Accuracy.

A network structure for industrial process fault diagnosis based on hyper feature extraction and stacked LSTM

Traditional feature extraction methods are highly dependent on manual work, and manual extraction will inevitably ignore some potentially useful features. For complex systems, there is no simple one-to-one correspondence between faults and symptoms, and fault characteristics often have complex characteristics such as hierarchy, propagation correlation, and delay. In the existing research results, the convolutional neural network (CNN) has been proven to be an effective network structure. But the existing network structure is more like a classifier, and only some features of industrial data are considered in the feature extraction stage, which will cause some feature information to be lost during the training process. To effectively solve the above problems, this paper proposes an industrial process-oriented hyper feature extraction network structure. This method first extracts multi-level CNN fault features, aggregates the hierarchical feature maps and compresses them in a unified feature space to form hyper features. Then the hyper features formed by feature fusion are input to the stacked Long Short Term Memory (LSTM) network for further feature extraction. In this way, the information in the time series can be effectively transmitted, and at the same time, the problem of gradient disappearance caused by long-term training can be solved. We conducted experiments on the Tennessee-Eastman (TE) benchmark process and industrial coking oven datasets. The experimental results show that the proposed method can effectively improve the fault diagnosis accuracy of the industrial production process system.

Critical Situations and Prevention of Accidents in Chemico-Technological Systems (Methodological Aspects)

The aim of this work is to study the causes of accidents in chemical processes, develop a methodology for accident prevention via control, and illustrat its realization by examples using a variety of strategies. The general concept of critical situations was introduced systematically covering both emergency and pre-emergency situations. In large-scale chemical plants, examples of accidents are presented. Accident causes as a result of disturbances and control faults in technological processes are analyzed. Approaches for preventing accidents are considered. The revealing of critical situations is presented as a problem of pattern recognition, and the subtasks of the recognition are analyzed. An emergency scale based on the assessment of various states of the chemico-technological process is introduced and applied for distinguishing the different levels of accident. The real obstacles in the prevention of accidents via control are shown and analyzed. Matrices of critical situations with corresponding characteristics are given. The main tasks for the prevention and elimination of critical situations are highlighted and characterized, and our methodology for the realization of these tasks is presented. Practical examples of the prevention of accidents in the industrial ammonia synthesis processes (including approaches and strategies) are demonstrated based on the real-time control of autothermal reactors.

Diagnostic Errors in Hospitalized Adults Who Died or Were Transferred to Intensive Care

Diagnostic errors contribute to patient harm, though few data exist to describe their prevalence or underlying causes among medical inpatients. To determine the prevalence, underlying cause, and harms of diagnostic errors among hospitalized adults transferred to an intensive care unit (ICU) or who died. Retrospective cohort study conducted at 29 academic medical centers in the US in a random sample of adults hospitalized with general medical conditions and who were transferred to an ICU, died, or both from January 1 to December 31, 2019. Each record was reviewed by 2 trained clinicians to determine whether a diagnostic error occurred (ie, missed or delayed diagnosis), identify diagnostic process faults, and classify harms. Multivariable models estimated association between process faults and diagnostic error. Opportunity for diagnostic error reduction associated with each fault was estimated using the adjusted proportion attributable fraction (aPAF). Data analysis was performed from April through September 2023. Whether or not a diagnostic error took place, the frequency of underlying causes of errors, and harms associated with those errors. Of 2428 patient records at 29 hospitals that underwent review (mean [SD] patient age, 63.9 [17.0] years; 1107 [45.6%] female and 1321 male individuals [54.4%]), 550 patients (23.0%; 95% CI, 20.9%-25.3%) had experienced a diagnostic error. Errors were judged to have contributed to temporary harm, permanent harm, or death in 436 patients (17.8%; 95% CI, 15.9%-19.8%); among the 1863 patients who died, diagnostic error was judged to have contributed to death in 121 (6.6%; 95% CI, 5.3%-8.2%). In multivariable models examining process faults associated with any diagnostic error, patient assessment problems (aPAF, 21.4%; 95% CI, 16.4%-26.4%) and problems with test ordering and interpretation (aPAF, 19.9%; 95% CI, 14.7%-25.1%) had the highest opportunity to reduce diagnostic errors; similar ranking was seen in multivariable models examining harmful diagnostic errors. In this cohort study, diagnostic errors in hospitalized adults who died or were transferred to the ICU were common and associated with patient harm. Problems with choosing and interpreting tests and the processes involved with clinician assessment are high-priority areas for improvement efforts.

Improved HLT Framework for Belle II Experiment

Improved HLT Framework for Belle II Experiment