Fault Diagnostic Equipment of Fire Control System Based on FTA

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.

In view of the complexity of the electronic control system of adaptive optics, combining with fault tree analysis and expert system, we design an expert system on fault diagnosis for electronic control system of adaptive optics based on Fault Tree. After analysis of electric control system of AO, fault tree can be established. The expert system is the extensive application of the fault tree. The system uses forward inference method to diagnose failure of system. In addition to traditional expert systems, point of failure was set to obtain real-time fault information. Then the information was sent to diagnosis expert systems to get the online function of diagnosis of system. The system highly improved the stability of AO system.

With the continuous upgrading and transformation of the intelligentisation of China's manufacturing industry, and in response to the requirements for further intelligentisation of the phosphor copper ball production line proposed by a new electronic material company, this study proposes a fault prediction and diagnosis method based on big data. A high‐efficiency distributed big data platform is constructed, and a workshop‐level monitoring centre with the Windows control centre (WinCC) as the core is formed. The WinCC configuration software is used to monitor the key parameters of the equipment during the operation phase, and the login interface is configured according to the requirements of workshop information integration, for example, display interface, alarm interface, debugging interface, trend graph and other common functions. Cloud platforms and virtual private network (VPN) communication are used to realise remote maintenance. Aiming at the common fault problems in the production process, an expert diagnosis system based on fault tree analysis is constructed by fusing the fault tree theory and expert systems. The fault tree model of the unqualified phosphor copper ball production quality and the failure of the hydraulic system is highlighted. Therefore, ensuring the safety of the phosphor copper ball production line is of great significance to the entire production system.

Fault tree analysis is a probability-based technique for estimating the risk of an undesired top event, typically a system failure. Traditionally, building a fault tree requires involvement of knowledgeable experts from different fields, relevant for the system under study. Nowadays’ systems, however, integrate numerous Internet of Things (IoT) devices and are able to generate large amounts of data that can be utilized to extract fault trees that reflect the true fault-related behavior of the corresponding systems. This is especially relevant as systems typically change their behaviors during their lifetimes, rendering initial fault trees obsolete. For this reason, we are interested in extracting fault trees from data that is generated from systems during their lifetimes. We present DDFTAnb algorithm for learning fault trees of systems using time series data from observed faults, enhanced with Naïve Bayes classifiers for estimating the future fault-related behavior of the system for unobserved combinations of basic events, where the state of the top event is unknown. Our proposed algorithm extracts repairable fault trees from multinomial time series data, classifies the top event for the unseen combinations of basic events, and then uses proxel-based simulation to estimate the system’s reliability. We, furthermore, assess the sensitivity of our algorithm to different percentages of data availabilities. Results indicate DDFTAnb’s high performance for low levels of data availability, however, when there are sufficient or high amounts of data, there is no need for classifying the top event.

Fault Tree Analysis (FTA) is a method that directly focuses on the modes of failures. The FTA is a graphical representation of the major faults or critical failures associated with a product, as well as the causes for the faults and potential countermeasures. The aim of this research paper is to calculate the probability of the top event – the failure of the process using FTA and propose a technique to prioritize factors for action design and reduce the likelihood of a top event failure based on manufacturers' requirements. We have constructed a qualitative fault tree to produce office components packed and sealed in blister packs using a KOCH KBS-PL machine. We defined the top event G – the production of office components, packed and sealed in blister packs on the machinery KOCH KBS-PL. Then we defined events leading to top events down to individual failure factors. Based on the links between the fault tree and the probability of failure, we performed a quantitative analysis to determine the probability of failure of individual events. We found out that the probability of failure of G is 5.04%. Subsequently, we identified which factors most significantly reduce the resulting probability of failure of factor G. These are the factors: E – feed rate, F – cooling, AL – incorrect setting and D – break. It has been proven that by controlling these 4 factors, we can reduce the probability of failure of top event G to 2.36%, provided that effective measures are taken. The final proposal meets the requirements of several manufacturers for a fast, efficient, and cost-effective solution. We have created a proposal that saves time, has minimal software and hardware requirements, and is easy to use. The efficiency and effectiveness of the proposal was that we identified the weakest points in the fault tree that most significantly cause the top event to fail. This prioritized the factors for the design of the measures.

The battery system, as the core energy storage device of new energy vehicles, faces increasing safety issues and threats. An accurate and robust fault diagnosis technique is crucial to guarantee the safe, reliable, and robust operation of lithium-ion batteries. However, in battery systems, various faults are difficult to diagnose and isolate due to their similar features and internal coupling relationships. In this paper, the current research of advanced battery system fault diagnosis technology is reviewed. Firstly, the existing types of battery faults are introduced in detail, where cell faults include progressive and sudden faults, and system faults include a sensor, management system, and connection component faults. Then, the fault mechanisms are described, including overcharge, overdischarge, overheat, overcool, large rate charge and discharge, and inconsistency. The existing fault diagnosis methods are divided into four main types. The current research and development of model-based, data-driven, knowledge-based, and statistical analysis-based methods for fault diagnosis are summarized. Finally, the future development trend of battery fault diagnosis technology is prospected. This paper provides a comprehensive insight into the fault and defect diagnosis of lithium-ion batteries for electric vehicles, aiming to promote the further development of new energy vehicles.

System fault diagnosis is often performed by humans using fault diagnosis knowledge such as fault trees and system state information. An experiment was conducted to investigate how various forms of presenting fault trees and system state information affect human performance and preference during fault diagnosis. Three knowledge presentations (a semantic network, a schema-based semantic network, and production rules) and four state-information presentations (direct manipulation, menus, fill-in-forms, commands) were examined in the experiment; 30 subjects participated. The results of the experiment indicated that knowledge presentation by the semantic network yielded better subject performance of fault diagnosis. In addition, direct manipulation was the most favorite form of state information presentation to the subjects. The s-significant interaction between the knowledge presentations and the state information presentations in terms of subject preference was also found.

Uncertainty in fault tree analysis: A fuzzy approach

This paper introduces a new method to design expert system reasoning with FPGA for fault diagnosis. Firstly, detailed analysis the reasoning process of normal expert system; Secondly, the experiences and knowledge of experts are transformed to multi-valued or binary reasoning with fault tree, and the processes are realized with simple gate circuits; Lastly, the new scheme is used in fault diagnosis for HV circuit breakers instead of primary reasoning with software. The experiment indicates that the reasoning speed using this scheme is faster than traditional reasoning modes, and it can be applicable to many expert systems based on single-chip controller or DSP (Digital Signal Processor).

<div class="htmlview paragraph">Fault tree analysis (FTA) evolved from the aerospace industry in the 1960’s. A fault tree is deductive logic model that is generated with a top undesired event in mind. FTA answers the question, “how can something occur?” as opposed to failure modes and effects analysis (FMEA) that is inductive and answers the question, “what if?” FTA is used in risk, reliability and safety assessments. FTA is currently being used by several industries such as nuclear power and chemical processing. Typically the automotive industries uses failure modes and effects analysis (FMEA) such as design FMEAs and process FMEAs. The use of FTA has spread to the automotive industry. This paper discusses the use of FTA for automotive applications. With the addition automotive electronics for various applications in systems such as engine/power control, cruise control and braking/traction, FTA is well suited to address failure modes within these systems. FTA can determine the importance of these failure modes from various perspectives such as cost, reliability and safety. A fault tree analysis of a car starting system is presented as an example.</div>

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.

Fault tree (FT) is usually a reliability and security analysis and diagnoses decision model. It is also in common use that expressing fault diagnosis question with fault tree model. But it will not be changed easily if fault free model was built, and it could not accept and deal with new information easily. It is difficult to put the information which have nothing to do with equipment fault but can be used to fault diagnosis into diagnostic course. Bayesian Networks (BN) can learn and improve its network architecture and parameters at any time by way of practice accumulation, and raises the ability of fault diagnosis. The method of building BN based on FT is researched on this article, this method could break through the limitations of FT itself, make BN be more extensively applied to the domain of fault diagnosis and gains much better ability of fault analysis and diagnosis.

Due to the limited generality of traditional fault diagnosis expert system and its low accuracy of extracting failure symptoms, a general fault monitoring and diagnosis expert system has been built. For different devices, users can build fault trees in an interactive way and then the fault trees will be saved as expert knowledge. A variety of sensors are fixed to monitor the real-time condition of the device and intelligent algorithms such as wavelet transform and neural network are used to assist the extraction of failure symptoms. On the basis of integration of multi-sensor failure symptoms, the fault diagnosis is realized through forward and backward reasoning. The simulation diagnosis experiments of NC device have shown the effectiveness of the proposed method.

Fault tree (FT) is a standardized notation for representing relationships between a system’s reliability and the faults and/or the events associated with it. However, the existing FT fault models are only capable of portraying permanent events in the system. This is a major hindrance since these models fail to reflect accurately the other classes of faults, such as soft-faults, which are often temporary events that usually disappear after the source of the interference is no longer present. This paper proposes a new fault tree modeling paradigm, to capture the impact of temporal events in systems, called temporal dynamic fault trees (TDFTs) . TDFTs are utilized to model the characteristics and dependencies between different temporal events, soft-faults, and permanent faults. These features are integrated into the proposed probabilistic models of the temporal gates, which are modeled as priced-timed automata . This paper also proposes a new FT analysis methodology, based on statistical model checking , designed to circumvent the state-explosion problem that is inherent to other model-checking approaches. The proposed analysis is able to evaluate the impact of temporal faults in systems, as well as to estimate the reliability and availability of the system over extended periods of time. The experiments reported in this paper demonstrate the versatility and scalability of the proposed approach. For instance, the results display the impact that temporal events may have in a digital system. Our observations indicate that while regular soft-fault analyses tend to underestimate metrics such as system reliability, TDFT analysis shows remarkable consistency with radiation testing, with differences of under 2%, in the conducted analysis.

Expert System of Fault Diagnosis for Gear Box in Wind Turbine