Biblio

This paper addresses analysis and design of multiple fault diagnosis for a class of Lipschitz nonlinear system. In order to automatically estimate multi-fault parameters efficiently, a new method of multi-fault diagnosis based on the differential evolution algorithm (DE) is proposed. Finally, a series of experiments validate the feasibility and effectiveness of the proposed method. The simulation show the high accuracy of the proposed strategies in multiple abrupt faults diagnosis.

The choice of threshold is an important part of fault diagnosis. Most of the current methods use a constant threshold for detection and it is difficult to meet the robustness and sensitivity requirements of the diagnosis system. This article develops a dynamic threshold algorithm for aircraft engine fault detection and isolation systems. The algorithm firstly analyzes the bounded norm uncertainty that may appear in the process of model based on the state space equation, and gives the time domain response range calculation formula under the influence of uncertain parameters; then the Kalman filter is combined to calculate the threshold with the real-time change of state; the simulation is performed at the end. The simulation results show that dynamic threshold range changes with status in real time.

Electric powertrains are widely used in automotive and renewable energy industries. Reliable diagnosis for defects in the critical components such as bearings, gears and stator windings, is important to prevent failures and enhance the system reliability and power availability. Most of existing fault diagnosis methods are based on specific characteristic frequencies to single faults at constant speed operations. Once multiple faults occur in the system, such a method may not detect the faults effectively and may give false alarms. Furthermore, variable speed operations render a challenge of analysing nonstationary signals. In this work, a deep learning-based fault diagnosis method is proposed to detect common faults in the electric powertrains. The proposed method is based on pattern recognition using convolutional neural network to detect effectively not only single faults at constant speed but also multiple faults in variable speed operations. The effectiveness of the proposed method is validated via an in-house experimental setup.

Principal Component Analysis (PCA) is a basic method of fault diagnosis based on multivariate statistical analysis. It utilizes the linear correlation between multiple process variables to implement process fault diagnosis and has been widely used. Traditional PCA fault diagnosis ignores the impact of faults with different magnitudes on detection accuracy. Based on a variety of data processing methods, this paper proposes a multi-level and multi-granularity principal component analysis method to make the detection results more accurate.

The NOC Network on chip provides better performance and scalability communication structures point-to-point signal node, shared through bus architecture. Information analysis of method using the IOT termination, as the energy consumed in this regard reduces and reduces the network load but it also displays safety concerns because the valuation data is stored or transmitted to the network in various stages of the node. Using encryption to protect data on the area of network-on-chip Analysis Machine is a way to solve data security issues. We propose a Network on chip based on a combined multicore cluster with special packages for computing-intensive data processing and encryption functionality and support for software, in a tight power envelope for analyzing and coordinating integrated encryption. Programming for regular computing tasks is the challenge of efficient and secure data analysis for IOT end-end applications while providing full-functionality with high efficiency and low power to satisfy the needs of multiple processing applications. Applications provide a substantial parallel, so they can also use NOC's ability. Applications must compose in. This system controls the movement of the packets through the network. As network on chip (NOC) systems become more prevalent in the processing unit. Routers and interconnection networks are the main components of NOC. This system controls the movement of packets over the network. Chip (NOC) networks are very backward for the network processing unit. Guides and Link Networks are critical elements of the NOC. Therefore, these areas require less access and power consumption, so we can better understand environmental and energy transactions. In this manner, a low-area and efficient NOC framework were proposed by removing virtual channels.

In a diagnosability verifier with polynomial complexity, a non-diagnosable system generates uncertain loops. Such forbidden loops are in this paper transformed to forbidden states by simple detector automata. The forbidden state problem is trivially transformed to a nonblocking problem by considering all states except the forbidden ones as marked states. This transformation is combined with one of the most efficient abstractions for modular systems called conflict equivalence, where nonblocking properties are preserved. In the resulting abstraction, local events are hidden and more local events are achieved when subsystems are synchronized. This incremental abstraction is applied to a scalable production system, including parallel lines where buffers and machines in each line include some typical failures and feedback flows. For this modular system, the proposed diagnosability algorithm shows great results, where diagnosability of systems including millions of states is analyzed in less than a second.

Present networks within safety-critical systems rely on complex and inflexible network configurations. New technologies such as software-defined networking are more dynamic and offer more flexibility, but due care needs to be exercised to ensure that safety and security are not compromised by incorrect configurations. To this end, this paper proposes the use of pre-generated and optimized configuration templates. These provide alternate routes for traffic considering availability, resilience and timing constraints where network components fail due to attacks or faults.To obtain these templates, two heuristics based on Dijkstra's algorithm and an optimization algorithm providing the maximum resilience were investigated. While the configurations obtained through optimization yield appropriate templates, the heuristics investigated are not suitable to obtain configuration templates, since they cannot fulfill all requirements.

The dynamic nature of the wireless channel poses a challenge to services requiring seamless and uniform network quality of service (QoS). Self-healing, a promising approach under the self-organizing networks (SON) paradigm, and has been shown to deal with unexpected network faults in cellular networks. In this paper, we use simple machine learning (ML) algorithms inspired by SON developments in cellular networks. Evaluation results show that the proposed approach identifies the faulty APs. Our proposed approach improves throughput by 63.6% and reduces packet loss rate by 16.6% compared with standard 802.11.

Heterogeneous Networks (Het Nets) are introduced to fulfill the increasing demands of wireless communications. To be manageable, it is expected that these networks are self-organized and in particular, self-healing to detect and relief faults autonomously. In the Cooperative Cell Outage Detection (COD), the Macro-Base Station (MBS) and a group of Femto-Base Stations (FBSs) in a specific range are cooperatively communicating to find out if each FBS is working properly or not. In this paper, we discuss the impacts of the cooperation range on the detection delay and accuracy and then conclude that there is an optimal amount for cooperation range which maximizes detection accuracy. We then derive the optimal cooperative range that improves the detection accuracy by using network parameters such as FBS's transmission power, noise power, shadowing fading factor, and path-loss exponent and investigate the impacts of these parameters on the optimal cooperative range. The simulation results show the optimal cooperative range that we proposed maximizes the detection accuracy.

The autonomic logistic system (ALS), first used by the U.S. military JSF, is a new conceptional system which supports prognostic and health management system of aircrafts, including such as real-time failure monitoring, remaining useful life prediction and maintenance decisions-making. However, the development of ALS faces some challenges. Firstly, current ALS is mainly based on client/server architecture, which is very complex in a large-scale aircraft control center and software is required to be reconfigured for every accessed node, which will increase the cost and decrease the expandability of deployment for large scale aircraft control centers. Secondly, interpretation of telemetry parameters from the aircraft is a tough task considering various real-time flight conditions, including instructions from controllers, work statements of single machines or machine groups, and intrinsic physical meaning of telemetry parameters. It is troublesome to meet the expectation of full representing the relationship between faults and tests without a standard model. Finally, typical diagnostic algorithms based on dependency matrix are inefficient, especially the temporal waste when dealing with thousands of test points and fault modes, for the reason that the time complexity will increase exponentially as dependency matrix expansion. Under this situation, this paper proposed a distributed ALS under complex operating conditions, which has the following contributions 1) introducing a distributed system based on browser/server architecture, which is divided overall system into primary control system and diagnostic and health assessment platform; 2) designing a novel interface for modelling the interpretation rules of telemetry parameters and the relationship between faults and tests in consideration of multiple elements of aircraft conditions; 3) proposing a promoted diagnostic algorithm under parallel computing in order to decrease the computing time complexity. what's more, this paper develops a construction with 3D viewer of aircraft for user to locate fault points and presents repairment instructions for maintenance personnels based on Interactive Electronic Technical Manual, which supports both online and offline. A practice in a certain aircraft demonstrated the efficiency of improved diagnostic algorithm and proposed ALS.

False alarm and miss are two general kinds of alarm errors and they can decrease operator's trust in the alarm system. Specifically, there are two different forms of trust in such systems, represented by two kinds of responses to alarms in this research. One is compliance and the other is reliance. Besides false alarm and miss, the two responses are differentially affected by properties of the alarm system, situational factors or operator factors. However, most of the existing studies have qualitatively analyzed the relationship between a single variable and the two responses. In this research, all available experimental studies are identified through database searches using keyword "compliance and reliance" without restriction on year of publication to December 2017. Six relevant studies and fifty-two sets of key data are obtained as the data base of this research. Furthermore, neural network is adopted as a tool to establish the quantitative relationship between multiple factors and the two forms of trust, respectively. The result will be of great significance to further study the influence of human decision making on the overall fault detection rate and the false alarm rate of the human machine system.

Design for Testability (DfT) techniques allow devices to be tested at various levels of the manufacturing process. Scan architecture is a dominantly used DfT technique, which supports a high level of fault coverage, observability and controllability. However, scan architecture can be used by hardware attackers to gain critical information stored within the device. The security threats due to an unrestricted access provided by scan architecture has to be addressed to ensure hardware security. In this work, a solution based on the Clock and Data Recovery (CDR) method has been presented to authenticate users and limit the access to the scan architecture to authorized users. As compared to the available solution the proposed method presents a robust performance and reduces the area overhead by more than 10%.

The engine is a significant and dynamic component of the aircraft. Because of the complicated structure and severe operating environment, the fault detection of the engine has always been the key and difficult issue in the field of reliability. Based on an engine and the acoustic emission technology, we propose a method of identifying fault types and determining different components in the engine by constructing the attenuation coefficient. There are several common faults of engines, and three different types of fault sources are generated experimentally in this work. Then the fault signal of the above fault sources propagating in different engine components are obtained. Finally, the acoustic emission characteristics of the fault signal are extracted and judged by the attenuation coefficient. The work effectively identifies different types of faults and studies the effects of different structural components on the propagation of fault acoustic emission signals, which provides a method for the use of acoustic emission technology to identify the faults types of the engine and to study the propagation characteristics of AE signals on the engine.*

Many accidents occurred frequently in aerospace applications, traditional software reliability analysis methods are not enough for modern flight control software. Developing a comprehensive, effective and intelligent method for software fault diagnosis is urgent for airborne software engineering. Under this background, we constructed a fault diagnosis expert system for flight control software which combines software failure mode and effect analysis with software fault tree analysis. To simplify the analysis, the software fault knowledge of four modules is acquired by reliability analysis methods. Then by taking full advantage of the CLIPS shell, knowledge representation and inference engine can be realized smoothly. Finally, we integrated CLIPS into VC++ to achieve visualization, fault diagnosis and inference for flight control software can be performed in the human-computer interaction interface. The results illustrate that the system is able to diagnose software fault, analysis the reasons and present some reasonable solutions like a human expert.

The following topics are dealt with: feature extraction; data mining; support vector machines; mobile computing; photovoltaic power systems; mean square error methods; fault diagnosis; natural language processing; control system synthesis; and Internet of Things.

The evolution of convolutional neural networks (CNNs) into more complex forms of organization, with additional layers, larger convolutions and increasing connections, established the state-of-the-art in terms of accuracy errors for detection and classification challenges in images. Moreover, as they evolved to a point where Gigabytes of memory are required for their operation, we have reached a stage where it becomes fundamental to understand how their inference capabilities can be impaired if data elements somehow become corrupted in memory. This paper introduces fault-injection in these systems by simulating failing bit-cells in hardware memories brought on by relaxing the 100% reliable operation assumption. We analyze the behavior of these networks calculating inference under severe fault-injection rates and apply fault mitigation strategies to improve on the CNNs resilience. For the MNIST dataset, we show that 8x less memory is required for the feature maps memory space, and that in sub-100% reliable operation, fault-injection rates up to 10-1 (with most significant bit protection) can withstand only a 1% error probability degradation. Furthermore, considering the offload of the feature maps memory to an embedded dynamic RAM (eDRAM) system, using technology nodes from 65 down to 28 nm, up to 73 80% improved power efficiency can be obtained.

This paper combines FMEA and n2 approaches in order to create a methodology to determine risks associated with the components of an underwater system. This methodology is based on defining the risk level related to each one of the components and interfaces that belong to a complex underwater system. As far as the authors know, this approach has not been reported before. The resulting information from the mentioned procedures is combined to find the system's critical elements and interfaces that are most affected by each failure mode. Finally, a calculation is performed to determine the severity level of each failure mode based on the system's critical elements.

Root cause analysis (RCA) is a common and recurring task performed by operators of cellular networks. It is done mainly to keep customers satisfied with the quality of offered services and to maximize return on investment (ROI) by minimizing and where possible eliminating the root causes of faults in cellular networks. Currently, the actual detection and diagnosis of faults or potential faults is still a manual and slow process often carried out by network experts who manually analyze and correlate various pieces of network data such as, alarms, call traces, configuration management (CM) and key performance indicator (KPI) data in order to come up with the most probable root cause of a given network fault. In this paper, we propose an automated fault detection and diagnosis solution called adaptive root cause analysis (ARCA). The solution uses measurements and other network data together with Bayesian network theory to perform automated evidence based RCA. Compared to the current common practice, our solution is faster due to automation of the entire RCA process. The solution is also cheaper because it needs fewer or no personnel in order to operate and it improves efficiency through domain knowledge reuse during adaptive learning. As it uses a probabilistic Bayesian classifier, it can work with incomplete data and it can handle large datasets with complex probability combinations. Experimental results from stratified synthesized data affirmatively validate the feasibility of using such a solution as a key part of self-healing (SH) especially in emerging self-organizing network (SON) based solutions in LTE Advanced (LTE-A) and 5G.

The modular multilevel converter with series and parallel connectivity was shown to provide advantages in several industrial applications. Its reliability largely depends on the absence of failures in the power semiconductors. We propose and analyze a fault-diagnosis technique to identify shorted switches based on features generated through wavelet transform of the converter output and subsequent classification in support vector machines. The multi-class support vector machine is trained with multiple recordings of the output of each fault condition as well as the converter under normal operation. Simulation results reveal that the proposed method has high classification latency and high robustness. Except for the monitoring of the output, which is required for the converter control in any case, this method does not require additional module sensors.

This paper presents a novel sensor parameter fault diagnosis method for generally multiple-input multiple-output (MIMO) affine nonlinear systems based on adaptive observer. Firstly, the affine nonlinear systems are transformed into the particular systems via diffeomorphic transformation using Lie derivative. Then, based on the techniques of high-gain observer and adaptive estimation, an adaptive observer structure is designed with simple method for jointly estimating the states and the unknown parameters in the output equation of the nonlinear systems. And an algorithm of the fault estimation is derived. The global exponential convergence of the proposed observer is proved succinctly. Also the proposed method can be applied to the fault diagnosis of generally affine nonlinear systems directly by the reversibility of aforementioned coordinate transformation. Finally, a numerical example is presented to illustrate the efficiency of the proposed fault diagnosis scheme.

Complex systems are prevalent in many fields such as finance, security and industry. A fundamental problem in system management is to perform diagnosis in case of system failure such that the causal anomalies, i.e., root causes, can be identified for system debugging and repair. Recently, invariant network has proven a powerful tool in characterizing complex system behaviors. In an invariant network, a node represents a system component, and an edge indicates a stable interaction between two components. Recent approaches have shown that by modeling fault propagation in the invariant network, causal anomalies can be effectively discovered. Despite their success, the existing methods have a major limitation: they typically assume there is only a single and global fault propagation in the entire network. However, in real-world large-scale complex systems, it's more common for multiple fault propagations to grow simultaneously and locally within different node clusters and jointly define the system failure status. Inspired by this key observation, we propose a two-phase framework to identify and rank causal anomalies. In the first phase, a probabilistic clustering is performed to uncover impaired node clusters in the invariant network. Then, in the second phase, a low-rank network diffusion model is designed to backtrack causal anomalies in different impaired clusters. Extensive experimental results on real-life datasets demonstrate the effectiveness of our method.

As a consequence of the recent development of situational awareness technologies for smart grids, the gathering and analysis of data from multiple sources offer a significant opportunity for enhanced fault diagnosis. In order to achieve improved accuracy for both fault detection and classification, a novel combined data analytics technique is presented and demonstrated in this paper. The proposed technique is based on a segmented approach to Bayesian modelling that provides probabilistic graphical representations of both electrical power and data communication networks. In this manner, the reliability of both the data communication and electrical power networks are considered in order to improve overall power system transmission line fault diagnosis.

In Energy Internet mode, a large number of alarm information is generated when equipment exception and multiple faults in large power grid, which seriously affects the information collection, fault analysis and delays the accident treatment for the monitors. To this point, this paper proposed a method for power grid monitoring to monitor and diagnose fault in real time, constructed the equipment fault logical model based on five section alarm information, built the standard fault information set, realized fault information optimization, fault equipment location, fault type diagnosis, false-report message and missing-report message analysis using matching algorithm. The validity and practicality of the proposed method by an actual case was verified, which can shorten the time of obtaining and analyzing fault information, accelerate the progress of accident treatment, ensure the safe and stable operation of power grid.

Transmission lines' monitoring systems produce a large amount of data that hinders faults diagnosis. For this reason, approaches that can acquire and automatically interpret the information coming from lines' monitoring are needed. Furthermore, human errors stemming from operator dependent real-time decision need to be reduced. In this paper a multiple faults diagnosis method to determine transmission lines' operating conditions is proposed. Different scenarios, including insulator chains contamination with different types and concentrations of pollutants were modeled by equivalents circuits. Their performance were characterized by leakage current (LC) measurements and related to specific fault modes. Features extraction's algorithm relying on the difference between normal and faulty conditions were used to define qualitative trends for the diagnosis of various fault modes.

This paper presents a solution to a multiple-model based stochastic active fault diagnosis problem over the infinite-time horizon. A general additive detection cost criterion is considered to reflect the objectives. Since the system state is unknown, the design consists of a perfect state information reformulation and optimization problem solution by approximate dynamic programming. An adaptive particle filter state estimation algorithm based on the efficient sample size is proposed to maintain the estimate quality while reducing computational costs. A reduction of information statistics of the state is carried out using non-resampled particles to make the solution feasible. Simulation results illustrate the effectiveness of the proposed design.