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]

The liquid−liquid two-phase flow and size prediction of slug droplets in flow-focusing microchannels with different downstream orifice sizes were investigated experimentally. Aqueous solution of 50%-glycerol and mineral oil with 4 wt.% surfactant sorbitanlauric acid ester (Span 20) were used as the dispersed and continuous phases, respectively. Three characteristic flow patterns were identified: slug flow, dripping flow, and jetting flow. The slug flow region decreased but the jetting flow region increased with the decrease in the size of the channel orifice. Afterwards, the universal flow pattern maps of the liquid−liquid two-phase in three microchannels were obtained based on dimensionless analysis. Furthermore, two slug droplet formation regions were found: when φ−1Cac < 0.01, the droplet formation was mainly driven by the squeezing force Fp, while when φ−1Cac > 0.01, both the squeezing force Fp and shear force Fτ contributed to droplet formation. Additionally, the prediction correl... [more]

Due to variables like wellbore deviation variation and flow rate, the local flow velocity in the output wellbore of horizontal shale oil wells varied significantly at various points in the wellbore cross-section, making it challenging to calculate the total single-layer production with accuracy. The oil−water two-phase flow rate calculation techniques for horizontal wells developed based on particular flow patterns and array spinners had excellent applicability in their respective niches but suffered from poor generalizability and demanding experience levels for logging interpreters. In this study, we employed five spinners in a triangular walled array instrument to create the multi-decision tree after figuring out how many leaf nodes there were and examining the defining characteristics of the observed values gathered under various experimental setups. The construction of the entire oil−water two-phase flow prediction model was made possible when the random forest regression approach... [more]

The accurate acquisition of downhole engineering parameters, such as real-time pressure and temperature measurements, plays a crucial role in mitigating drilling risks and preventing accidents. In this study, we present the design of a real-time data acquisition and transmission system for drilling operations. The system utilizes a near-bit measurement method to simultaneously measure downhole parameters, including mud pressure and temperature. By analyzing the pressure and temperature frequencies obtained from a quartz crystal pressure gauge and compensating for temperature effects, accurate pressure values are obtained. The resistance value of a PT1000 sensor is measured, and a second-order fitting is performed using laboratory scale coefficients to determine the temperature values. The data acquisition system employs an advanced microcontroller as the main control chip, along with an A/D conversion chip. Additionally, signal amplification, data storage modules, data transmission mod... [more]

Electrical systems consist of varied components that are used for power distribution, supply, and transfer. During transmission, component failures occur as a result of signal interruptions and peak utilization. Therefore, fault diagnosis should be performed to prevent fluctuations in the power distribution. This article proposes a fluctuation-reducing fault diagnosis method (FRFDM) for use in power distribution networks. The designed method employs fuzzy linear inferences to identify fluctuations in electrical signals that occur due to peak load demand and signal interruptions. The fuzzy process identifies the fluctuations in electrical signals that occur during distribution intervals. The linear relationship between two peak wavelets throughout the intervals are verified across successive distribution phases. In this paper, non-recurrent validation for these fluctuations is considered based on the limits found between the power drop and failure. This modification is used for preventi... [more]

In order to overcome the shortcomings of existing electrowetting display defect detection models in terms of computational complexity, structural complexity, detection speed, and detection accuracy, this article proposes an improved YOLOv7-based electrowetting display defect detection model. The model effectively optimizes the detection performance of display defects, especially small target defects, by integrating GhostNetV2 modules, Acmix attention mechanisms, and NGWD (Normalized Gaussian Wasserstein Distance) Loss. At the same time, it reduces the parameter size of the network model and improves the inference efficiency of the network. This article evaluates the performance of an improved model using a self-constructed electrowetting display defect dataset. The experimental results show that the proposed improved model achieves an average detection rate (mAP) of 89.5% and an average inference time of 35.9 ms. Compared to the original network, the number of parameters and computatio... [more]

As the global market becomes increasingly competitive and demanding, companies face the challenge of responding swiftly and efficiently to customer needs. To maintain a competitive advantage, organisations must optimise the usage of their assets. This study focuses on the critical role of maintenance management and presents a novel, cost-effective, and easily applicable model that integrates Industry 4.0 (I4.0) and Total Productive Maintenance (TPM) principles to enhance production processes. The proposed model incorporates a real-time monitoring system equipped with sensors, a gateway, and Internet of Things (IoT) services. These components enable data acquisition, transmission, storage, and visualisation through both mobile and fixed devices. The model’s effectiveness was validated through its implementation on a conveyor belt in a feed mill. The availability of the conveyor belt was around 89.5% before TPM implementation. After the implementation of TPM, it was possible to observe t... [more]

Orthonormal subspace analysis (OSA) is proposed for handling the subspace decomposition issue and the principal component selection issue in traditional key performance indicator (KPI)-related process monitoring methods such as partial least squares (PLS) and canonical correlation analysis (CCA). However, it is not appropriate to apply the static OSA algorithm to a dynamic process since OSA pays no attention to the auto-correlation relationships in variables. Therefore, a novel dynamic OSA (DOSA) algorithm is proposed to capture the auto-correlative behavior of process variables on the basis of monitoring KPIs accurately. This study also discusses whether it is necessary to expand the dimension of both the process variables matrix and the KPI matrix in DOSA. The test results in a mathematical model and the Tennessee Eastman (TE) process show that DOSA can address the dynamic issue and retain the advantages of OSA.

In this investigation, an automotive component made of nylon as a structural element was studied by several characterization techniques to identify material properties. Firstly, a Fourier transform infrared spectroscopy (FTIR) was carried out to obtain information about composition, then, differential scanning calorimetry (DSC) was used to extract useful information on sample thermal behavior. The humidity and volatile materials percentage could be assessed by thermogravimetry analysis (TGA). Morphology and topography were carried out by optical microscopy, moreover, X-ray Tomography allows it to display the sample’s inner part. Characterization shows that the component could have been contaminated or exposed to conditions that promote degradation after the manufacturing process. Finally, computerized X-ray tomography displayed that both samples showed a difference in porosity in a fractured sample and a healthy sample. All the above implies a change in the mechanical integrity of the... [more]

In today’s rapidly evolving manufacturing landscape with the advent of intelligent technologies, ensuring smooth equipment operation and fostering stable business growth rely heavily on accurate early fault detection and timely maintenance. Machine learning techniques have proven to be effective in detecting faults in modern production processes. Among various machine learning algorithms, the Backpropagation (BP) neural network is a commonly used model for fault detection. However, due to the intricacies of the BP neural network training process and the challenges posed by local minima, it has certain limitations in practical applications, which hinder its ability to meet efficiency and accuracy requirements in real-world scenarios. This paper aims to optimize BP networks and develop more effective fault warning methods. The primary contribution of this research is the proposal of a novel hybrid algorithm that combines a random wandering strategy within the main loop of an equilibrium... [more]