Ontology-based Fault Diagnosis

In order to meet the requirements of multi-parameter and real-time fault diagnosis of China VI vehicle emission standards diesel engine air system, this paper focuses on the research of the diesel engine air system fault detection and diagnosis method based on deep learning to improve the operational safety and fault diagnosis efficiency. In this paper, the air system fault detection and diagnosis are completed based on the AE model and the CNN model, combined with the actual operation of the diesel engine. Among them, the AE model successfully detected all real-time operation faults, and the false detection rate on the health data was 0.1162%; the CNN model obtained a 90.77% fault diagnosis accuracy rate on the test set of the real-time operation data set. The results show that the model has high accuracy in the diagnosis of diesel engine faults, which is of great significance to the application of deep learning based on big data processing.

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.

This paper proposes a new method for fault diagnosis of new energy transmission system based on combined BiLSTM-1DCNN model. The EV dataset is preprocessed and converted into a format acceptable to the model. The BiLSTM neural network is used to extract temporal features, and the 1DCNN network is used to capture the spatial features of the data. The combination of the two networks can effectively improve the accuracy and efficiency of fault diagnosis. Comparing the proposed method with traditional machine learning methods, it is found that the proposed method has higher accuracy and faster convergence speed. Sensitivity analysis is used to study the impact of different hyperparameters on model performance. The results show that the performance of the model is affected by the number of layers, learning rate and window size in the 1DCNN network. In addition, the experiments and datasets of this study are from ADVISOR of the US Department of Energy (DOE). The proposed combined BiLSTM-1DCNN model provides a promising method for fault diagnosis of new energy drive systems, which can improve the efficiency and accuracy of fault diagnosis.

The traditional fault diagnosis expert system is dependent on knowledge acquisition of the experts. Knowledge acquisition is recognized as the problem of expert system. In addition, there are also some limitations of adaptive capacity, learning ability and real-time. And artificial neural network with good fault-tolerance and associative memory function, as well as very strong self-adaptive, self-learning ability, just can make up for the limitations of traditional expert system. This paper will construct a new expert system with the artificial neural network into and fault tree. Besides fault tree and neural network, this article mainly introduces the system model of fault diagnosis of the fire control computer and sensor subsystem, the method and process of fault diagnosis. In this expert system, we use the object-oriented production rule to represent the knowledge, which solves the bottleneck problem of the diagnostic knowledge acquisition effectively. The inferential process begins with the abnormal event and finally finds all of the possible faults and the faulty component. For some possible faulty components, which have large number of fault samples, the neural network model can be used to diagnose. The training network of fault samples employs the BP neural network. Finally, simulation training results show that the fault diagnosis expert system based on the combination of fault tree and neural network is rational and effective in fault diagnosis of the fire control system, realizes perfectly the combination of new knowledge and old one, and can grasp the state of systems dynamically.

The technology of integrated health management (IHVM) is an important approach to improve the security, reliability and maintainability of airplanes and spacecrafts, and to reduce their cost significantly. Taken aircraft electric power system into consideration, it is one of the important equipment that provides electric energy for all electro-equipments in airplane. Its working state is a crucial to ensure airplane normal and secure flight, so the research of condition detection, fault diagnosis and prognosis is a great concern in the field of IHVM system. Aiming at interoperability and complexity of data exchanging for aircraft electric power diagnosis and prognostic system, a method of using ATML (automatic test markup language) based on XML (extensible markup language) is proposed in order to describe the information of devices and conditions for fault diagnosis and prognostic in the system. First, based on reference frame of integrated health management system, a distributed multi-level fault diagnosis and prognosis system structure that exchange data through ATML is designed. Secondly, condition monitor is a foundation of fault diagnosis and prognosis, and a schema of test information for aircraft electric power parameters is designed by using ATML after data of crucial fault is analyzed. The result shows that the method can implement seamless interaction among test, diagnosis and prognosis. At same time, sharing and transplant of test information and diagnostic knowledge is improved and the process of fault diagnosis is accelerated.

Considering the characteristic of fault diagnosis of large steam turbine unit for performance monitoring, a method for performance monitoring and fault diagnosis was designed based on versatile logical form. The logical form was proposed according to fault tree analysis. The diagnostic program can implement the entire process of fault analysis and diagnosis depending on the logical form. This method has characteristics of easy operation and good universality. Design idea and working process of the method were introduced by means of low feedwater temperature.

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

This work presents a framework for fault detection and diagnosis of multimode industrial processes. It is shown that the proposed method to generate features for the faults provides a generalization that can allow the fault diagnosis in modes for which no training was performed. The proposed method is applied to a sophisticated simulator of a continuous stirred tank reactor (CSTR). Experimental results suggest the usefulness of this approach for the detection of faults under operating conditions not considered during training phase.

Buildings are subject to faults in their heating, ventilation and air-conditioning (HVAC) systems that can lead to excessive energy wastage, poor indoor climate, equipment failures and high maintenance costs. Field studies have shown that employing fault detection, diagnosis and prognosis (FDDP) tools followed up with equipment services and corrections can help achieve up to 40% of energy savings within the HVAC system and improve indoor climate, increase equipment lifecycle and reduce maintenance costs. The increasing adoption of building automation systems (BAS), Internet of Things (IoT) and other smart technologies in recent years have allowed large amounts of data to be continuously collected from building systems. This data-rich environment, along with the surge in data analytics and machine learning tools, has made cost-effective data-driven FDDP strategies possible. Compared to purely physics-based methods, data-driven methods require less explicit knowledge of the underlying physical system, thus are often easier to develop, and can learn certain intricate relationships that exist among data. Within the reported data-driven FDDP methods, there exists a few research gaps: 1) data imputation methods that leverage mutual information from correlated measurements to defy poor data quality from BAS have not been utilized efficiently; 2) there lacks a systematic and scalable fault diagnosis framework that incorporates probabilistic temporal relationships to track fault evolution; 3) existing fault diagnosis strategies typically focus on traditional rule-based control strategies and their scalability for advanced control strategies such as Guideline 36 have not been explored yet; 4) active threats information such as cyber-attacks, are typically not incorporated in an FDDP framework; 5) fault prognosis strategies to preemptively identify gradual faults for predictive maintenance have rarely been studied. This research attempts to address the above-mentioned research gaps through the following: Data Imputation: reported data imputation methods that are suitable for handling and repairing multi-source BAS data are evaluated. Data collected from a medium-sized, mixed-use institution building situated in Stockholm, Sweden and a small commercial building simulated in a laboratory setup is used to evaluate five different data imputation methods. Results demonstrate that incorporating time-lagged cross-correlations within the k-Nearest Neighbor (kNN) method helps to significantly improve the imputation accuracy and minimize the impact of repaired data on data-driven algorithms. Dynamic Bayesian Network (DBN)-based Framework for Cyber-Physical Fault Diagnosis: a DBN framework with discretized conditional probabilities parameters to represent the temporal relationships among building measurements is developed. Both domain knowledge and machine learning methods are used to develop the DBN structure and parameter model. The developed framework is evaluated for both traditional rule-based and Guideline 36 controls using datasets from a real building, a laboratory building, and a virtual testbed. Results show that the developed DBN framework is effective in diagnosing and isolating faults in systems even with different control strategies. The framework also successfully distinguishes whether system abnormalities originate from cyber-attacks or naturally occurring physical faults. Potential future direction to improve fault isolation using modified DBN topological structure is also reported in this study. DBN-based Framework for Fault Prognosis: an extension of the DBN framework in conjunction with Robust Multivariate Temporal (RMT) variate selection is proposed for fault prognosis. The RMT variate selection is used to extract localized temporal features from high dimensional datasets to determine the best inputs for training forecasting models. The expected fault-free behavior of multiple target variates, selected using domain knowledge, is forecasted using incoming data. The prediction errors generated from the forecasting phase are used as evidences in the DBN inference to estimate future fault probabilities. Gradual faults simulated in the virtual testbed are used to evaluate the prognosis framework. Results show that the developed framework is effective in prognosing gradual faults by leveraging the trending growth on the prediction errors. The research presented in this thesis contributes to the overall objective of developing a robust and cost-effective DBN-based framework for fault diagnosis and prognosis of building HVAC systems. Potential solutions to other existing challenges of implementing data-driven FDDP strategies, such as obtaining high-quality datasets, handling and repairing missing data, establishing a baseline model for detecting abnormalities despite other disturbances such as weather and internal conditions changes, and extracting temporal features from timeseries data are also examined.

This paper focuses on the research of data-driven fault diagnosis models and proposes a data-driven degradation fault diagnosis method for civil aircraft landing gear brake systems. Time-series segments with abnormal points are extracted from QAR data of historical faulty flight segments, and fault data labeling is completed by combining with airline ground maintenance records. In constructing the multi-classification fault model, a hierarchical training strategy is employed. Models such as random forests are used to analyze feature importance, gradually constructing a fault feature set, and machine learning or deep learning models are employed to train the multi-classification model. Meanwhile, the SHAP analysis method is integrated to develop an interpretable output model. In the fault diagnosis process, time-series features are extracted based on the fault feature set and input into the multi-classification model to predict fault causes, which are then interpreted and output by the SHAP model. Experimental results show that the method achieves over 92% diagnostic accuracy for faults such as BCU faults and sensor faults, providing an efficient and reliable solution for degradation fault diagnosis of landing gear brake systems and helping improve the safety of civil aircraft operations.

SUMMARYWith the development of wireless mesh networks (WMNs), fault diagnosis in WMNs is becoming a very challenging task. In this paper, a two‐level scheme for fault diagnosis in WMNs is presented. We partition the network into a two‐level topology architecture where level 1 is composed of mesh clients and level 2 consists of mesh routers. A new comparison approach is introduced to diagnose the two levels. On the basis of the new comparison approach, every node in WMNs can be diagnosed either as fault‐free or faulty. Our protocol assumes that the WMN's topology may change during the testing phase and utilize the shortest path spanning tree, which is constructed along with the process of fault diagnosis, to disseminate local messages and global messages in WMNs. The proposed model is only for static fault circumstances. We provide the analysis of correctness, communication complexity, and time complexity of our protocol, and the comparison between our protocol and others through both theoretical proof and practical simulation. The analysis shows that our model has significant advantages over other existing models. Copyright © 2012 John Wiley & Sons, Ltd.

Fault of the equipment is an attribute set described by multiple values. Rough Set (RS) technology was applied to find potential dependency model from a great quantities of history data, thus, to give the expression to fragmented and indefinite knowledge among status and fault information of equipment. In order to realize the function of remote service and fault diagnosis, we constructed public service platform based on Internet, which was shared by multi-manufacturer and multi-customer. The method was adopted which combined the expert system based on knowledge with application instance in diagnosis center. Under the support of dependency model based on data mining, the process of fault diagnosis was finished according to equipment state. This paper set forth the architecture of the system, running model, system function, the decision-making process for diagnosis, the data mining arithmetic based on RS, appraisal and implementation of system.

Time fault propagation graph (TFPG) is a directed graph model to describe the dynamic causality of the system. According to TFPG model, the system fault can be inferred from the observed events and the fault time can be estimated. In this paper, the process of fault diagnosis using time fault propagation graph is researched, and TFPG fault diagnosis algorithm framework is designed. In order to improve the effectiveness of fault diagnosis, the improved hypothesis initialization algorithm based on event and improved hypothesis update algorithm based on incremental reasoning are developed in the paper. Finally, we take Right Inboard Power Control Unit (PCU-RIB) as an example. According to the system characteristics and fault types, the TFPG model of PCU-RIB is constructed. Then the improved fault diagnosis algorithm based on TFPG is used for simulation verification. The results show that the improved algorithm has good robustness, can correctly eliminate the interference of false alarms and identify the corresponding fault.KeywordsFault diagnosisTime fault propagation diagramHealth managementTime consistency

Efficient condition monitoring and fault diagnosis is an essential task to ensure the generation performance and reliability of photovoltaic (PV) systems. This paper proposes an online algorithm to diagnose faults of PV module based on multi-class support vector machine (M-SVM). The simulation models of the photovoltaic module are implemented and the output power generation characteristics of PV modules under two typical fault conditions (line-to-line fault and abnormal degradation fault) are analyzed. Through the combination of mathematical model and simulation experiments of PV modules, the fill factor (FF) and the fault type factor K are introduced as feature parameters in the fault diagnosis process. In addition, to address the nonlinear problem in fault diagnosis and improve the single support vector machine, a fault diagnosis method based on the multi-class classification method of one-against-one (OAO) algorithm is proposed. The proposed solution is implemented and simulated through experiments and the numerical result clearly demonstrates its effectiveness and diagnosis accuracy.

Adaptive fusion graph convolutional network based interpretable fault diagnosis method for HVAC systems enhanced by unlabeled data