Fault Diagnosis for Batch Processes by Improved Multi-model Fisher Discriminant Analysis

Intelligence modeling interpretable for incipient fault diagnosis of batch process represents a serious challenge. For the multivariate, nonlinear, and high-dimensionality characteristics of process data, existing fault diagnosis solutions are easily concealed by noise while neglect the low amplitude and noise interference of the incipient faults. In this paper, an intelligent incipient fault diagnosis model based on deep time series feature extraction network is proposed, which integrates denoising autoencoder (DAE), layer normalization and dropout layer added LSTM (LD-LSTM), and stacked autoencoder (SAE) to obtain the efficient features. DAE is applied to restore the data and eliminate noise interference. LD-LSTM is utilized to extract time series features, in which the layer normalization and the dropout layer are added between the layers of the LSTM to solve the problems of slow model convergence and over-fitting. SAE performs the feature extraction to obtain deep time series features and learns more efficient expressions. The softmax function is applied to achieve the incipient fault diagnosis based on the deep time series features. A case study on a fed-batch penicillin fermentation process suggests that the proposed method can obtain an accuracy rate of 98.97%, which has increased an average of 20% than the traditional shallow machine learning. The accuracy of fault recognition and comparative experiments demonstrate the effectiveness and feasibility of this model, and provide a solution for the application of intelligent fault diagnosis in batch industries.

This paper investigates fault diagnosis in batch processes and presents a comparative study of feature extraction and classification techniques applied to a specific biotechnological case study: the fermentation process model by Birol et al. (Comput Chem Eng 26:1553-1565, 2002), which is a benchmark for advanced batch processes monitoring, diagnosis and control. Fault diagnosis is achieved using four approaches on four different process scenarios based on the different levels of noise so as to evaluate their effects on the performance. Each approach combines a feature extraction method, either multi-way principal component analysis (MPCA) or multi-way independent component analysis (MICA), with a classification method, either artificial neural network (ANN) or support vector machines (SVM). The performance obtained by the different approaches is assessed and discussed for a set of simulated faults under different scenarios. One of the faults (a loss in mixing power) could not be detected due to the minimal effect of mixing on the simulated data. The remaining faults could be easily diagnosed and the subsequent discussion provides practical insight into the selection and use of the available techniques to specific applications. Irrespective of the classification algorithm, MPCA renders better results than MICA, hence the diagnosis performance proves to be more sensitive to the selection of the feature extraction technique.

Various faults occurred in the continuous bulk materials weighing equipment (CBMWE) usually lead to more economic loss and waste of human resources inevitably. A new approach based on the improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering and Bayesian regularization neural network (BRNN) is proposed for online fault detection and diagnosis of CBMWE--electronic belt weigher (BW). Firstly, in view of the fault data varying with equipment flow, an improved DBSCAN clustering algorithm is developed to realize the online fault detection by extracting the fault data with the clustering analysis of the real-time data. Secondly, BRNN is proposed as a classifier to identify the fault pattern with the extracted fault data. Both the models of online fault detection and diagnosis are realized using MATLAB. Finally, the test result shows that the proposed online fault detection and diagnosis model is able to cope with the online fault detection and diagnosis of BW and also yields great diagnostic accuracy. In general, this approach for online fault detection and diagnosis of BW has a great significance to bulk weighing equipment.DOI: http://dx.doi.org/10.5755/j01.mech.21.1.8560

Comprehensive subspace decomposition and isolation of principal reconstruction directions for online fault diagnosis

Tensor dynamic neighborhood preserving embedding algorithm for fault diagnosis of batch process

The electrical system fault testing and diagnosis system based on I2C-bus and embedded equipment is proposed. This system is accomplished with the integration of PXI-based computer, field fault testing platform, dedicated interface adapter unit and accessories with software program of virtual instrument and database management and fault guidance system. It can obtain various state parameters of the heavy mechanized bridge by interface adapter unit and corresponding sensors. The dedicated interface adapter unit, that is the core of the hardware platform is composed of chassis, embedded microcomputer controller, I2C-bus data acquisition modules, VPC connection package and military aviation sockets. Because of its flexibility and modular design, it can support different data conversion, for example, analog, digital, and serial, while functioning as an intelligent conversion bridge between field fault testing platform or PXI computer and electrical system of heavy mechanized bridge electrical system. The fault diagnosis software is of hierarchical modular structure with integration of system management control, fault detection and circuit hardware driver modules together with fault repair and diagnosis database. Therefore it provides functions of human-machine interaction, equipment fault detection, fault diagnosis and analysis, as well as repair and maintenance guidance The fault testing and diagnosis system in this work is also featured with handy structure, lightweight, low consumption, large storage capacity, excellent expandability and intelligent inference capability. The system can be used in the online fault testing and diagnosis of similar engineering equipments. So it has important engineering and military application value to the rapid fault testing and fault diagnosis of engineering equipments.

Focusing on quality-related complex industrial process performance monitoring, a novel multimode process monitoring method is proposed in this paper. Firstly, principal component space clustering is implemented under the guidance of quality variables. Through extraction of model tags, clustering information of original training data can be acquired. Secondly, according to multimode characteristics of process data, the monitoring model integrated Gaussian mixture model with total projection to latent structures is effective after building the covariance description form. The multimode total projection to latent structures (MTPLS) model is the foundation of problem solving about quality-related monitoring for multimode processes. Then, a comprehensive statistics index is defined which is based on the posterior probability of the monitored samples belonging to each Gaussian component in the Bayesian theory. After that, a combined index is constructed for process monitoring. Finally, motivated by the application of traditional contribution plot in fault diagnosis, a gradient contribution rate is applied for analyzing the variation of variable contribution rate along samples. Our method can ensure the implementation of online fault monitoring and diagnosis for multimode processes. Performances of the whole proposed scheme are verified in a real industrial, hot strip mill process (HSMP) compared with some existing methods.

Fault identification and diagnosis of steam turbine generator unit is very important for safely and economically operation of a power plant. Currently, on-line monitoring is already widely utilized for alarming and recording in steam turbine. However, large amount of on-line monitoring data is not fully utilized to realize on-line fault diagnosis and identification, especially multi-concurrent fault. In the present study, model-based method was used for on-line vibrational fault identification and diagnosis based on rotordynamics. A 660MW supercritical steam turbine rotor system was modeled using FEM. Single faults, such as mass unbalance, local shaft bow and transverse crack, and multi-concurrent faults were simulated. Fault-induced changes of equivalent loads were analyzed to figure out fault type, location and severity. For model-based method, fault diagnosis accuracy is influenced by the model accuracy and signal noise. Model sensitivity was studied in this research by comparing the influence of different model error and SNR (Signal to Noise Ratio). It was used to evaluate the degree of confidence of the diagnosis result. The smaller was the model sensitivity, the higher was the degree of confidence. Based on this research, model-based method was utilized to analyze real vibration signals extracted from the historical data of this unit. Fault identification result was an effective basis for conditioned based maintenance.

When an insulated-gate bipolar transistor (IGBT) open-circuit fault occurs, a three-phase pulse-width modulated (PWM) converter can usually keep working, which will lead to system instability and more serious secondary faults. The fault detection and diagnosis of the converter is extremely necessary to improve the reliability of the power supply system. In order to solve the problem of fault misdiagnosis caused by parameters disturbance, this paper proposes a robust accuracy weighted random forests online fault diagnosis model to accurately locate various IGBTs open-circuit faults. Firstly, the fault signal features are preprocessed by using the three-phase current signal and normalization method. Based on the test accuracy of the perturbed out-of-bag data and the multiple converters test data, a robust accuracy weighted random forests algorithm is proposed for extracting a mapping relationship between fault modes and current signal. In order to further improve the fault diagnosis performance, a parameter optimization model is built to optimize hyper-parameters of the proposed method. Finally, comparative simulation and online fault diagnosis experiments are carried out, and the results demonstrate the effectiveness and superiority of the method.

This paper deals with information model for power grid fault diagnosis based on Common Information Model (CIM). Fault diagnosis gains information from topology information and device parameters of primary system grid based on IEC61970 standard and device information of secondary system grid based on IEC 61850. Device information contains information of protection movement wave record and protection configuration. However, the existent CIM can't fully represent power system fault diagnosis information. Thus, it is need to extend CIM. All kinds of information are based on different model, which causes different information standard. In this paper a preliminary study on CIM extensions which is based on power system fault diagnosis, has been proposed to perfect fault information model. On the basis of different standard model, this paper has discussed the theory of coordinating IEC 61850 and IEC61970 standard, and proposed using Web Ontology Language (OWL) based on Extensible Mark-up Language (XML) to the solution of this problem. A fault Information Model is then provided to the fault diagnosis system according to Component Interface Specification (CIS) in IEC61970. After receiving fault information, the fault diagnosis system starts for real-time, on-line fault diagnosis. Fault information model has solved the problem of the deficiency and non-standard of fault diagnosis information, attaining fault diagnosis information integrity and standardization, and making the fault diagnosis result fast and accurate.

Fault diagnosis is a critical activity in the comprehensive support for equipment operation. Relevant research about it is becoming more and more popular. Ontology-based fault diagnosis is an important method for diagnosing faults. Due to the expressive ability, knowledge sharing and knowledge reuse based on deep semantics, and supporting for logical reasoning, ontology-based fault diagnosis has received more and more attention from researchers in the past decade. The fault diagnosis ontology describes the core concepts involved in diagnosing faults and the relationship among the concepts. Its quality and usage determine the efficiency and effectiveness of fault diagnosis, thus has a great impact on fault diagnosis. However, its knowledge source and usage have not been analyzed comprehensively to give us a deep insight into this area yet. This paper investigates and studies the ontology-based fault diagnosis during the past decade, from the perspective of knowledge source and usage of fault diagnosis ontology. In summary, it can be seen that combining ontology with other methods improves the efficiency and the effect of fault diagnosis, and ontology-based fault diagnosis needs to be integrated into the whole process of fault diagnosis. To make knowledge of fault diagnosis complete, machine learning shall be used for the ontological engineering.

Fault diagnosis, including fault detection and isolation, is a critical task for computer networks. Among the various techniques used for online system-level diagnosis, we are interested in the approach based on temporal information processing. The delays of the computer networks are inevitable, and the fault localization process has to take into account bounded delays or the temporal constraints. Temporal information is fundamental in model-based diagnosis. There can be cycles or loops in a computer network, but the fault reasoning methods for such cases are seldom considered in the literature. This paper provides an analytic model based on the cyclic temporal constraint network (CTCN), which aims at the fault diagnosis of cyclic computer networks using temporal information. The goal of the proposed framework is twofold: given the network structures and the predetermined candidate fault causes, the CTCN model corresponding the computer network under test is formulated; based on the CTCN model, given the alarms sequences with timestamps, the fault diagnosis process is executed to determine the most likely fault cause(s) with its/their time interval(s) of occurrence(s). The reasoning method is dependent on time point and time distance information, with which the fault motivators (i.e., actors) and fault responders (i.e., victims) can be identified. The calculation process consists of three steps: 1) establishment of the objective function; 2) determination of the fault propagation paths; and 3) determination of the expected states with a given fault hypothesis. Finally, the proposed method is demonstrated via an application study, and the effectiveness of our proposed method is verified.

The concept of Industry 4.0 uses cyber-physical systems and the Internet of Things to create "smart factories" that enable automated and connected production. However, the complex and diverse nature of cyber-physical systems makes them vulnerable to physical threats, where any fault in their physical components can lead to serious consequences, from financial losses to human safety. To reduce the risk of such failures, it is essential to have techniques in place to monitor the condition of these physical components closely. In recent years, data-driven predictive maintenance methods have become common due to the growth in data and advancements in machine learning. This thesis focuses on fault diagnosis, a key part of predictive maintenance, which aims to detect and identify faults in physical components. Incorporating advanced techniques, including machine learning algorithms and sensor data analysis, enhanced fault diagnosis systems' accuracy and efficiency. However, several real-world barriers challenge the effectiveness of these techniques. This research is structured around three primary objectives. The first objective aims to establish an efficient fault diagnosis system that is highly sensitive to fault occurrences while being resilient to uncertainties such as variability in fault patterns and data imbalance. To address variability in fault patterns, we developed a multi-step approach integrating clustering techniques and an adaptive neuro-fuzzy inference system, which significantly improved diagnosis robustness even under noisy conditions. For imbalanced datasets, a two-step method was introduced, combining permutation entropy for fault detection and envelope analysis for fault isolation. This approach was enhanced by an automatic frequency band selection method leveraging wavelet transform and statistical criteria, resulting in high fault diagnosis accuracy and reliability, even with limited labeled faulty data. The second objective focuses on developing fault diagnosis systems that can maintain high performance despite domain shifts and limited training data in both source and target domains. To address the challenge of domain shifts, a transfer learning technique was introduced, and a systematic literature review was conducted. This study bridges a technical gap by providing a comprehensive taxonomy, state-of-the-art methods, and insights into the challenges of applying transfer learning to predictive maintenance. Additionally, a conceptual framework was proposed to explore scenarios for transferring diagnostic knowledge from different source domains to target domains with insufficient training datasets. In a subsequent study, a new diagnostic framework was developed using digital twins and partial transfer learning to address the lack of real training datasets in both source and target domains as well as label space inconsistencies between them. The framework includes a general approach for developing digital twins of real machines to generate synthetic diagnostic data as source domain data. Furthermore, by incorporating a deep learning network and a double-layer attention mechanism, a partial transfer learning diagnostic model was developed to transfer diagnostic knowledge from the digital twin to the real target domain despite label space inconsistencies. The proposed framework was validated through an industrial case study involving rolling bearings in rotating equipment. The third objective focuses on developing an efficient fault diagnosis system capable of maintaining reliability under unseen and evolving working conditions. To achieve this, a novel self-adaptive fault diagnosis system was proposed, incorporating the MAPE-K loop, a domain generalization network model, and digital twin technologies. This self-adaptive fault diagnosis system leverages the MAPE-K loop to adapt in real time to evolving working conditions. Digital twins were employed to continuously generate data aligned with the real system’s operational context, enabling accurate fault diagnosis in dynamic environments. The domain generalization network model enhances generalization to unseen target working conditions by addressing overfitting and feature loss through multi-domain data augmentation, adversarial learning, and domain discrepancy modules. Validation using multiple rotating machinery datasets demonstrated that the developed fault diagnosis system outperformed state-of-the-art methods across various scenarios. In summary, this dissertation proposes a comprehensive and practical approach to addressing variability in fault patterns, imbalance datasets, data scarcity, domain shifts, and unseen and evolving working conditions challenges in existing fault diagnosis systems.

Vacuum system is a pivotal system of space environment simulator whose safe and reliable operation is directly concerned with smooth implementation of the whole space environment simulation test. In order to accomplish online status monitoring and fault diagnosis for vacuum system, this paper proposes a hierarchical fuzzy Petri net-based fault diagnosis model for vacuum system. Since the fault with vacuum system is a typical discrete process, Petri net is used to model the fault diagnosis process for vacuum system as a discrete event system. Against the shortcomings of traditional Petri net in handling sophisticated systems such as exorbitant number of nodes and complicated model, the hierarchical fuzzy Petri net is utilized associated with fuzzy logic and the theory of hierarchical fault diagnosis to establish a detailed fault diagnosis model for vacuum system, and verifies the effectiveness of this model via example.

Dynamic model-based fault diagnosis for (bio)chemical batch processes