Improving the reliability of heuristic multiple fault diagnosis via the EC-based Genetic Algorithm

Graph-based systems are models wherein the nodes represent the components and the edges represent the fault propagation between the components. For critical systems, some components are equipped with smart sensors for on-board system health management. When an abnormal situation occurs, alarms will be triggered from these sensors. This paper considers the problem of identifying the set of potential failure sources from the set of ringing alarms in graph-based systems. However, the computational complexity of solving the optimal multiple fault diagnosis (MFD) problem is exponential. Based on Lagrangian relaxation and subgradient optimization, we present a heuristic algorithm to find approximately the most likely candidate fault set. A computationally cheaper heuristic algorithm - primal heuristic - has also been applied to the problem so that real-time MFD in systems with several thousand failure sources becomes feasible in a fraction of a second. This paper also considers systems with asymmetric and multivalued alarms (tests).

Genetic algorithms have demonstrated considerable success in providing good solutions to many NP-hard optimization problems. For such problems, exact algorithms that always find an optimal solution are only useful for small toy problems, so heuristic algorithms such as the genetic algorithm must be used in practice. In this paper, we apply the genetic algorithm to the NP-hard problem of multiple fault diagnosis (MFD). We compare a pure genetic algorithm with several variants that include local improvement operators. These operators, which are often domain-specific, are used to accelerate the genetic algorithm in converging on optimal solutions. Our empirical results indicate that by using the appropriate local improvement operator, the genetic algorithm is able to find an optimal solution in all but a tiny fraction of the cases and at a speed orders of magnitude faster than exact algorithms.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

We review the recent work done by our group on theoretically analyzing the behavior of genetic algorithms(GAs) and searching for new ideas and strategies to design more efficient GAs. By introducing the concept of degree of population diversity and using the tools from finite Markov chain, we presented fairly clear characterizations of premature convergence in GAs and proved some results on the relationships between premature convergence and GA’s control parameters such as the population size, the mutation and crossover probabilities, and some population statistics. In the mean time, we have also obtained a result that depicts the search ability of the crossover operators in GAs. An upper bound on the convergence rate for the GAs has been established. Based on the theoretical analyses, we have proposed a novel GA called Genetic Algorithm with No Genetic Operators (GANGO), which needs neither to maintain a population nor to use the conventional genetic operators, and yet has the same search mechanisms as the classical GAs. As another development in improving the performance of GAs, a new concept called Dual Fitness Function was proposed which, when incorporated into the classical GAs will enable the algorithms to judge whether the current population is in the local or global attractive basins. And in this way, the control parameters of GAs can be adjusted adaptively. Some applications of our results to fields such as fuzzy systems, multi-destination routing problem (MDR) are briefly

Aiming at solving the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new multiple fault diagnosis method based on the dependency model method as well as the knowledge in test results and the prior probability of each fault type is proposed. Firstly, the dependency model of the system can be built and used to formulate the fault-test dependency matrix. Then, the dependency matrix is simplified according to the knowledge in the test results of the system. After that, the logic ‘OR’ operation is performed on the feature vectors of the fault status in the simplified dependency matrix to formulate the multiple fault dependency matrix. Finally, fault diagnosis is based on the multiple fault dependency matrix and the ranking of each fault type calculated basing on the prior probability of each fault status. An illustrative numerical example and a case study are presented to verify the effectiveness and superiority of the proposed method.

Shipyards have large departments or facilities. It is essential to make an effective topological layout plan since the initial investment cost of these departments is high. Topological layout is an optimization problem and Genetic Algorithm (GA) is generally used in the literature. The selection of effective genetic algorithm approaches and operators are very important to improve the performance of the optimization. This study investigates an effective solution to the shipyard topological layout using a Quadratic Assignment Problem (QAP) model with classic and elitist GA approaches. Besides, genetic operators that have significant effects on exploitation and exploration capabilities are analyzed. Therefore, 126 experiments were run with 13 different operators. The results obtained from the classic and elitist GA approach were evaluated individually and compared with each other. It was observed that the elitist GA approach has a superior performance compared to the classic GA approach. This study is the most comprehensive and practical study on the performance of the GA for topological layout of the shipyard in the literature.

A step-wise genetic algorithm (GA)-based approach is put forward for (a) guiding any interested country on how to optimally improve its environmental sustainability (ES), and (b) determining the characteristics of the (theoretically) maximally sustainable country. The Environmental Sustainability Index (ESI) 2005 is used to this end, with the GA genes, chromosomes, and fitness values corresponding to the ESI 2005 indicators, sets of indicator values, and scores, respectively. Following GA operation, any interested country can increase — and, ideally, maximise — its ES via the following procedure: a chromosome with similar gene values to the ESI indicator values of the interested country, but demonstrating higher fitness (ESI score), is appropriately selected from any GA generation such that the changes that are necessary in order for the country's ESI indicator values to match those of the selected chromosome to be not only implementable, but furthermore capable of producing the maximal improvement in terms of ES. By repeating this procedure on each updated version of the interested country, the country's indicator values and ESI score are progressively modified towards maximal ES. Alternative optimisation approaches, such as pattern search (PS) and simulated annealing (SA), are also capable of reaching maximal ES, yet — due to their single-state basis of operation — do not permit the concurrent investigation of alternative paths towards maximal ES, and — furthermore — do not guarantee the identification of the appropriate indicator(s) to be modified. An interesting finding that is common to GA, PS, and SA operation is that the fitness values of the best chromosomes/states invariably exceed not only the ESI score of the top-ranking ESI 2005 country (75.1 of Finland), but — furthermore — the “perfect” ESI 2005 score of 100 by around 10, thus putting into question the construction details of the ESI 2005.1

A hybrid algorithm (HA) integrated genetic algorithm (GA) with ant colony optimization (ACO) for solving Traveling Salesman Problems(TSP) was studied to get better optimization performance than each single algorithm, and complement merits each other and avoid each own demerits. The hybrid algorithm runs GA first and then ACO. A new strategy called GSA was proposed aiming at the key link in the HA that converts genetic solution from GA into information pheromone to distribute in ACO. GSA takes new matrix which is formed by the combination of the former 90% of individual from genetic solution and 10% of individual by random generation as the basis of transformation of pheromone value. The best combination of genetic operators in GA was also discussed. Several TSP were used as simulation tests to test genetic operators matching and optimization performance of HA. The results show that PMX crossover matched with IVM mutation in the GA is the best combination of genetic operators which is able to make GA improve the precision of optimal solution, and HA using the best combination operators and GSA strategy is successful and available to search for optimal solution in high efficiency and has good convergence.

The closest substring problem is a formal description of how to find a pattern such that from a given set of strings a subregion of each string is highly similar to that pattern. This problem appears frequently in computational biology and in coding theory. Experimental results suggest that this NP-hard optimization problem can be approached very well with a custom-built evolutionary algorithm using a fixed-length string representation, as in the typical genetic algorithm (GA) concept. Part of this success can be attributed to a novel mutation operator introduced in this paper. For practical purposes, the GA used here seems to be an improvement compared to traditional approximation algorithms. While the time complexity of traditional approximation algorithms can be analyzed precisely, they suffer from poor run-time efficiency or poor accuracy, or both.

The GA(genetic algorithms) was applied to find routing trees with minimal maximal sensor load thus extend network lifetime in wireless sensor networks. However, the existing algorithmlimits search space of GA to avoid generating cycles by dividing sensors into layers and stipulating that a sensor only can select a sensor in its lower layer as its new parent. In this paper, we have found that how cycles are generated during GA operations, and proposed a solution based on subtree to avoid generating cycles during GA operations.The solution only requires a sensor not to change its parent to a sensor in the subtree with it as the root. And layers are no longer considered. As a result, GA has more search space and GA operations can be performed more freely. Thus more optimal routing trees are obtained and network lifetime is extended. The experiment shows that our proposed algorithm extend network lifetime notably.

In this paper, a new approach to alarm processing in power systems is proposed based on the set covering theory and a refined genetic algorithm (RGA). Although alarm processing and fault diagnosis have different explanations in power system community, from the viewpoint of artificial intelligence community, the alarm processing problem is a typical Multiple Fault Diagnosis (MFD) problem. Thus, we first attribute the alarm processing problem to a MFD problem, and discuss the MFD problem in general with special emphasis on the evaluation criteria. And then, a probabilistic criterion is introduced to describe the alarm processing problem, and this criterion is more reasonable man the currently used criteria. Thirdly, a new method is presented to solve the alarm processing problem using a RAG. Finally, two sample examples are used to demonstrate the feasibility and efficiency of the developed method. Test results suggest that the developed RAG-based probabilistic method to the alarm processing problem is promising.

Multiple fault diagnosis is a challenging problem because the number of candidates grows exponentially in the number of faults. The multiple fault problems is important, since the single fault assumption can lead to incorrect or failed diagnoses when multiple faults occur. In this work, we present an approach for diagnosing multi faults based on model using techniques of detection and localization. We use an observer to generate residuals for a decision in a stage of monitoring and diagnostic system when disruptions or defects occur. Our contribution is the proposal of a diagnostic method when multiple faults type actuators or sensors affect the system.

Genetic Algorithms work iteratively from generation to generation to find the optimal solution of optimization problems. However, due to the probabilistic operations of Genetic Algorithms (GAs), the performance of GAs search is unpredictable. Even worst, GAs may not be able to find the optimal solution after very long iteration. We propose a solution that incorporates a human intervention to guide GA achieving a better performance. We adopt the Viral Trait Spreading Framework for human intervention in the GA operations. Firstly, we classify GAs operation and then put each group of operation in the Framework. Most of all genetic algorithm operations fall into the Trait Adoption component. We optimized the design of genetic representation and genetic operators to tackle the fixed element constraint and row permutation constraint of Sudoku puzzle. Then, we implemented our approach in net logo, a multiagent programmable modeling environment. Experiment results showed that GA is capable of finding the optimal solution and the human intervention through Viral Trait Spreading Framework guides the GA in searching processes in the narrower search space.

Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining.

Intensification and diversification are two driving forces in genetic algorithms and are frequently the subject of research. While it seemed for decades that a genetic operator can be classified as either the one or the other, it has been shown in the last few years that this assumption is an oversimplified view and most operators exhibit both, diversification and intensification, to some degree. Most papers in this field focus on a certain operator or algorithm configuration as theoretical and generalizable foundations are hard to obtain. In this paper we therefore use a wide range of different configurations and behavior measurements to study the intensification and diversification behavior of genetic algorithms and their operators.

With the development of the processing capacity of the embedded chip, it is possible to implement a machine learning algorithm in the embedded system. To achieve the fault status without poor portability, tricky threshold selection, and complex rulemaking, this article proposes a multimodal deep residual filter network (DRFN) for online multiple open-switch fault diagnosis of T-type three-level inverters. It contains low-rank matrix fusion (LMF), a DRFN, and a cross-transformer mechanism. The LMF fuses the voltage signal and the current signal for obtaining the unified representation. Then, the DRFN filters noise adaptively and extracts information effectively. Finally, the cross-transformer mechanism outputs the fault state of the T-type three-level inverter. The datasets consist of the dc-link voltage and load side current of the inverter control system. The data time window is selected as 20 ms. Through the real-time calculation of online monitored data, the experimental results show the effectiveness of the proposed fault diagnosis approach. Moreover, the accuracy of fault diagnosis is 99.18%, and the average open-circuit fault diagnosis time is 21 ms.