The scientific and reasonable maintenance strategy is critical to ensure the continuous operation of stationary equipment in the petrochemical industry; both risk-based inspection (RBI) and integrity operating windows (IOWs) are effective for stationary equipment maintenance. In traditional static RBI, the risk is assumed to remain constant in whole inspection period, and the latest variations of medium and process parameters are not fed back into risk calculation. Thus, risk value may be overestimated or underestimated, leading to unexpected failures or excessive inspection. Integrated application of dynamic risk-based inspection (DRBI) and IOWs is an advanced direction for this problem. However, due to the complexity of dynamic interaction mechanisms and risk assessment algorithms as well as the deficiency of powerful software, industrial applications of DRBI and IOWs are still limited. By proposing improved dynamic risk indexes and real-time monitoring process parameters as well as... [more]

The occurrence of tool chatter can have a detrimental impact on the quality of the workpiece. In order to improve surface quality, machining stability, and reduce tool wear cycles, it is essential to monitor the workpiece machining process in real time during the turning process. This paper presents a tool chatter state recognition model based on a denoising autoencoder (DAE) for feature dimensionality reduction and a bidirectional long short-term memory (BiLSTM) network. This study examines the feature dimensionality reduction method of the DAE, whereby the reduced-dimensional data are concatenated and input into the BiLSTM model for training. This approach reduces the learning difficulty of the network and enhances its anti-interference capability. Turning experiments were conducted on a SK50P lathe to collect the dataset for model performance validation. The experimental results and analysis indicate that the proposed DAE-BiLSTM model outperforms other models in terms of prediction... [more]

The following Special Issue entitled “Process Monitoring and Fault Diagnosis” aims to explore the latest progress and perspectives on the application of data analytic techniques to enhance stable operation and safety in chemical processes and other related process industries [...]

Normal fuzzy fault tree is a classic model in the field of process industry risk assessment, and it can provide reliable prior knowledge for machine learning. However, it is difficult to adapt the traditional approximate calculation method to highly nonlinear problems, and this may introduce model uncertainty. To solve this problem, this study proposes an accurate calculation algorithm. In the proposed algorithm, first, an exact α-cut set of normal fuzzy fault tree is derived according to the exact calculation formula of normal fuzzy numbers and in combination with the cut-set theorem. Subsequently, the relationship between the membership function and the exact cut set is derived based on the representation theorem. Finally, according to the previous derivation, the coordinates of the point on the exact membership curve are found within the range of x from 0 to 1. Based on this, an accurate membership graph is drawn, the membership curve is evenly divided with the area enclosed by the... [more]

Modern industrial processes are continuously monitored by a large number of sensors. Despite having access to large volumes of historical and online sensor data, industrial practitioners still face challenges in the era of Industry 4.0 in effectively utilizing them to perform online process monitoring and fast fault detection and diagnosis. To target these challenges, in this work, we present a novel framework named “FARM” for Fast, Accurate, and Robust online process Monitoring. FARM is a holistic monitoring framework that integrates (a) advanced multivariate statistical process control (SPC) for fast anomaly detection of nonparametric, heterogeneous data streams, and (b) modified support vector machine (SVM) for accurate and robust fault classification. Unlike existing general-purpose process monitoring frameworks, FARM’s unique hierarchical architecture decomposes process monitoring into two fault detection and diagnosis, each of which is conducted by targeted algorithms. Here, we t... [more]

In the field of industrial equipment reliability assessment, dependency on either degradation or failure time data is common. However, practical applications often reveal that single-type reliability data for certain industrial equipment are insufficient for a comprehensive assessment. This paper introduces a Bayesian-fusion-based methodology to enhance the reliability assessment of industrial equipment. Operating within the hierarchical Bayesian framework, the method innovatively combines the Wiener process with available degradation and failure time data. It further integrates a random effects model to capture individual differences among equipment units. The robustness and applicability of this proposed method are substantiated through an in-depth case study analysis.

The coal mine ground gas transportation system is widely used for gas transportation and mixing preheating in the gas storage and oxidation utilization system. However, gas or coal dust explosions may occur, which could result in heavy casualties and significant economic losses. To prevent accidents in the gas transportation system, the present study takes the gas transportation system of Shanxi Yiyang Energy Company as an example to identify the composition and hazardous factors of the gas transportation system. Fault tree analysis (FTA) models were established with pipeline gas and coal dust explosions as the top events, and the importance of each basic event was quantitatively analyzed using the fuzzy fault tree analysis (FFTA) method. The results show that gas and coal dust explosion accidents are mostly caused by the combination of high-temperature ignition sources and explosive materials. The uneven mixing gas and the ventilation carrying a large amount of coal dust are the funda... [more]

In this study, the influence of U-bends on the flow and pressure propagation characteristics of a gas−liquid two-phase flow in upstream and downstream straight pipes was investigated experimentally. The superficial velocities of the gas and liquid are 0.18−25.11 m/s and 0.20−1.98 m/s, respectively, covering plug flow, slug flow, and annular flow. The experiments were conducted in U-tubes with inner diameters of 9 mm and 12 mm and with a curvature ratio of 8.33. The U-tube was C-shaped. The pressure fluctuations at the axial measurement points of the straight tubes were measured. Flow images of the distal straight tubes and U-bends were obtained. The disturbance from U-bends in the two-phase flow in the vicinity of the bend is very obvious. The perturbation from U-bends in the fluid in the adjacent straight tubes is highly related to the incoming flow pattern. The slug flow has the most significant influence, whereas the effects of the plug and annular flows are small. Fundamentally, it... [more]

This paper presents a comprehensive review of the historical development, the current state of the art, and prospects of data-driven approaches for industrial process monitoring. The subject covers a vast and diverse range of works, which are compiled and critically evaluated based on the different perspectives they provide. Data-driven modeling techniques are surveyed and categorized into two main groups: multivariate statistics and machine learning. Representative models, namely principal component analysis, partial least squares and artificial neural networks, are detailed in a didactic manner. Topics not typically covered by other reviews, such as process data exploration and treatment, software and benchmarks availability, and real-world industrial implementations, are thoroughly analyzed. Finally, future research perspectives are discussed, covering aspects related to system performance, the significance and usefulness of the approaches, and the development environment. This work... [more]

Traditional two-dimensional dynamic fault detection methods describe nonlinear dynamics by constructing a two-dimensional sliding window in the batch and time directions. However, determining the shape of a two-dimensional sliding window for different phases can be challenging. Samples in the two-dimensional sliding windows are assigned equal importance before being utilized for feature engineering and statistical control. This will inevitably lead to redundancy in the input, complicating fault detection. This paper proposes a novel method named attention-based two-dimensional dynamic-scale graph autoencoder (2D-ADSGAE). Firstly, a new approach is introduced to construct a graph based on a predefined sliding window, taking into account the differences in importance and redundancy. Secondly, to address the training difficulties and adapt to the inherent heterogeneity typically present in the dynamics of a batch across both its time and batch directions, we devise a method to determine t... [more]

For the dynamic problem that the low-speed sliding table is unable to meet the radial measuring speed of the laser tracker, this paper takes the sliding table of the indoor large-length standard device as the moving object to double the measuring distance by adding a pyramid prism, thereby doubling the radial speed of the laser tracker. In this paper, the measurement data are analyzed through equal interval measurement experiments, equal sampling frequency experiments and repeatability measurement experiments using a pyramid prism to obtain the following conclusions, respectively: Firstly, the stability of the actual interval of the laser tracker is optimal when the rated speed of the sliding table is 50 mm/s. When the pyramid prism is not used, the minimum standard deviation obtained by the laser tracker at a sampling interval of 5 mm is 0.0158 mm. Secondly, during the equal sampling frequency measurement, the stability of the laser interferometer is better than that of the laser trac... [more]

This research introduces a new data-driven methodology for fault detection and isolation in dynamic systems, integrating fuzzy/Bayesian change point detection and decision trees induced by genetic programming for pattern classification. Tracking changes in sensor signals enables the detection of faults, and using decision trees generated by genetic programming allows for accurate categorization into specific fault classes. Change point detection utilizes a combination of fuzzy set theory and the Metropolis−Hastings algorithm. The primary contribution of the study lies in the development of a distinctive classification system, which results in a comprehensive and highly effective approach to fault detection and isolation. Validation is carried out using the Tennessee Eastman benchmark process as an experimental framework, ensuring a rigorous evaluation of the efficacy of the proposed methodology.

Traditional logging methods need a lot of data support such as suction profile information, reservoir geological information, and production information of injection and extraction wells to calculate oil and gas production, which is a tedious and complicated process with low interpretation accuracy. Distributed fiber optic vibration signal logging is a technology that uses fiber optics to sense the vibration signals returned from different formations or well walls to analyze the surrounding formation characteristics or downhole events, which has the advantages of strong real-time monitoring results and high reliability of interpretation results. However, the currently distributed fiber optic vibration signal logging also fails to fully utilize the technical advantages to form a systematic production calculation process. Therefore, this paper proposes to use the K-means++ algorithm to divide the vibration signal frequency bands to represent different downhole events and use the amplitud... [more]

The hydrodynamic characteristic of the multiphase mixed-transport pipeline is essential to guarantee safe and sustainable oil−gas transport when extracting offshore oil and gas resources. The gas−liquid two-phase transport phenomena lead to unstable flow, which significantly impacts pipeline deformation and can cause damage to the pipeline system. The formation mechanism of the mixed-transport pipeline slug flow faces significant challenges. This paper studies the formation mechanism of two-phase slug flows in mixed-transport pipelines with multiple inlet structures. A VOF-based gas−liquid slug flow mechanical model with multiple inlets is set up. With the volumetric force source term modifying strategy, the formation mechanism and flow patterns of slug flows are obtained. The research results show that the presented strategy and optimization design method can effectively simulate the formation and evolution trends of gas−liquid slug flows. Due to the convective shock process in the ei... [more]

This paper studies the use of varying threshold in the statistical process control (SPC) of batch processes. The motivation is driven by how when multiple phases are implicated in each repetition, the distributions of the features behind vary with phases or even the time; thus, it is inconsistent to uniformly bound them by an invariant threshold. In this paper, we paved a new path for learning and monitoring batch processes based on an efficient framework integrating a model termed conditional dynamic variational auto-encoder (CDVAE). Phase indicators are first used to split the data and are then separated, serving as an extra input for the model in order to alleviate the learning complexity. Dissimilar to the routine using features across all timescales, only features relevant to local timestamps are aggregated for threshold calculation, producing a varying threshold that is more specific for the process variations occurring among the timeline. Leveraged upon this idea, a fault detect... [more]

A three-dimensional Navier−Stokes and continuity equation model is employed to numerically predict the resonant modes of sessile droplets on horizontally vibrating substrates. A dynamic contact angle model is implemented to simulate the contact angle variations during vibrations. The four resonant modes (n = 1, 2, 3 and 4) of a droplet under horizontal vibrations are investigated. Simulations are compared to experimental results for validation. Excellent agreement is observed between predicted results and experiments. The model is used to simulate the internal flow patterns within the droplet under resonant modes. It is found that the flow in all four resonant modes can be divided into the Stokes region, the gas−liquid interface region, and the transition region located in between. Numerical simulations show that the average velocity within the droplet increases with the increase in frequency, while the fluctuations in average velocity after reaching the steady state show different tre... [more]

Vat photopolymerization is renowned for its high flexibility, efficiency, and precision in ceramic additive manufacturing. However, due to the impact of random defects during the recoating process, ensuring the yield of finished products is challenging. At present, the industry mainly relies on manual visual inspection to detect defects; this is an inefficient method. To address this limitation, this paper presents a method for ceramic vat photopolymerization defect detection based on a deep learning framework. The framework innovatively adopts a dual-branch object detection approach, where one branch utilizes a fully convolution network to extract the features from fused images and the other branch employs a differential Siamese network to extract the differential information between two consecutive layer images. Through the design of the dual branches, the decoupling of image feature layers and image spatial attention weights is achieved, thereby alleviating the impact of a few abnor... [more]

In order to detect abnormal heat generation in time, a reversible color-changing coating temperature measurement method is proposed for self-detection of temperature by power equipment, and its adhesion characteristics and aging performance were analyzed. The results showed that the reversible color-changing coating prepared with crystalline violet lactone as the colorant, bisphenol A as the color developer, octadecanol as the solvent, and RTV-II as the base paint can meet the requirements of self-detection of temperature by power equipment with its adhesion performance. The accelerated aging tests using high temperature, light and humidity were carried out in the laboratory, and we concluded that the deterioration degree of the color-changing coating was positively correlated with the temperature. Light can accelerate the aging rate of reversible color-changing coatings, and the degradation process of the coating was significantly accelerated under UV light. The effect of humidity on... [more]

The development of sensor networks allows for easier time series data acquisition in industrial production. Due to the redundancy and rapidity of industrial time series data, accurate anomaly detection is a complex and important problem for the efficient production of the textile process. This paper proposed a semantic inference method for anomaly detection by constructing the formal specifications of anomaly data, which can effectively detect exceptions in process industrial operations. Furthermore, our method provides a semantic interpretation of exception data. Hybrid signal temporal logic (HSTL) was proposed to improve the insufficient expressive ability of signal temporal logic (STL) systems. The epistemic formal specifications of fault offline were determined, and a data-driven semantic anomaly detector (SeAD) was constructed, which can be used for online anomaly detection, helping people understand the causes and effects of anomalies. Our proposed method was applied to time-seri... [more]

The reliability of an RMT can be regarded as an important indicator customers can use to recognize its quality; however, it is difficult to implement a full-state reliability analysis of an RMT due to its complicated structural functions. Therefore, a full-state reliability analysis model is proposed herein based on meta action (MA) and a Markov chain for remanufactured exercise machine tools (REMTs). First, an analysis was carried out on individual levels by integrating the MAU decomposition method, and an MAU fault tree model was established layer by layer for the REMT. Second, full-state modeling was performed in view of the MAU characteristics of the REMT, whose operation processes are divided into MAU normal and failure states. A Markov decision-making process was introduced to integrate MAU states and establish our model, which was solved by means of an analytical method for the evaluation of reliability. Finally, an example of a remanufactured machine tool spindle is given to ve... [more]

The process industry is confronted with rising demands for flexibility and efficiency. One way to achieve this is modular process plants, which consist of pre-manufactured modules with their own decentralized intelligence. Plants are then composed of these modules as unchangeable building blocks and can be easily re-configured for different products. Condition monitoring of such plants is necessary, but the available solutions are not applicable. The authors of this paper suggest an approach in which model-based symptoms are derived from a few measurements and observers that are based on the manufacturer’s knowledge. The comparisons of redundant observers lead to residuals that are classified to obtain symptoms. These symptoms can be communicated to the plant control and are inputs to an easily adaptable diagnosis. The implementation and validation at a modular mixing plant showcase the feasibility and potential of this approach.

The prevention and control of gas explosion accidents are important means to improving the level of coal mine safety, and risk assessment has a positive effect on eliminating the risk of gas explosions. Aiming at the shortcomings of current risk assessment methods in dynamic control, state expression and handling uncertainty, this study proposes a method combining fault tree analysis and fuzzy polymorphic Bayesian networks. The risk factors are divided into multiple states, the concept of accuracy is proposed to correct the subjectivity of fuzzy theory and Bayesian networks are relied on to calculate the risk probability and risk distribution in real time and to propose targeted prevention and control measures. The results show that the current risk probability of a gas explosion accident in Wangzhuang coal mine is as high as 35%, and among the risk factors, excessive ventilation resistance and spontaneous combustion of coal are sources of induced risk, and the sensitivity value of ele... [more]

To address the issues of strong subjectivity, low efficiency, and difficulty in on-site model deployment encountered in existing CCTV defect detection of pipelines, this article proposes an object detection model based on an improved YOLOv5s algorithm. Firstly, involution modules and GSConv simplified models are introduced into the backbone network and feature fusion network, respectively, to enhance the detection accuracy. Secondly, a CBAM attention mechanism is integrated to improve the detection accuracy of overlapping targets in complex backgrounds. Finally, knowledge distillation is performed on the improved model to further enhance its accuracy. Experimental results demonstrate that the improved YOLOv5s achieved an mAP@0.5 of 80.5%, which is a 2.4% increase over the baseline, and reduces the parameter and computation volume by 30.1% and 29.4%, respectively, with a detection speed of 75 FPS. This method offers good detection accuracy and robustness while ensuring real-time detecti... [more]

An algorithm based on the YOLOv5 model is proposed to address safety incidents such as tearing and blockage at transfer points on belt conveyors in coal mines caused by foreign objects mixed in with the coal flow. Given the tough underground conditions and images acquired with low quality, recursive filtering and MSRCR image enhancement algorithms were utilized to preprocess the dynamic images collected by underground monitoring devices, substantially enhancing image quality. The YOLOv5 model has been improved by introducing a multi-scale attention module (MSAM) during the channel map slicing, thereby increasing the model’s resistance to interference from redundant image features. Deep separable convolution was utilized in place of conventional convolution to detect, identify, and process large foreign objects on the belt conveyor as well as to increase detection speed. The MSAM-YOLOv5 model was trained before being installed on the NVIDIA Jetson Xavier NX platform and utilized to iden... [more]

Efficient monitoring of the blast furnace system is crucial for maintaining high production efficiency and ensuring product quality. This article introduces a hybrid cluster variational autoencoder model for monitoring the blast furnace ironmaking process which exhibits multimode behaviors. In contrast to traditional approaches, this method utilizes neural networks to learn data features and effectively handles the diverse feature types observed in different production modes. Through the utilization of a clustering process within the hidden layer of the variational autoencoder, the proposed technique facilitates efficient fault detection in the context of multimodal blast furnace data. Based on the variational autoencoder model, this study further establishes a unified monitoring index and defines a method for computing the control limits. The application of the model to real blast furnace data reveals its proficiency in accurately identifying faults across diverse modes; compared with... [more]