A distributed architecture to implement a prognostic function for complex systems

The proactivity in maintenance management is improved by the implementation of CBM (Condition-Based Maintenance) principles and of PHM (Prognostic and Health Management). These implementations use data about the health status of the systems. Among them, prognostic data make it possible to evaluate the future health of the systems. The Remaining Useful Lifetimes (RULs) of the components is frequently required to prognose systems. However, the availability of complex systems for productive tasks is often expressed in terms of RULs of functions and/or subsystems; those RULs have to bring information about the components. Indeed, the maintenance operators must know what components need maintenance actions in order to increase the RULs of the functions or subsystems, and consequently the availability of the complex systems for longer tasks or more productive tasks. This paper aims at defining a generic prognostic function of complex systems aiming at prognosing its functions and at enabling the isolation of components that needs maintenance actions. The proposed function requires knowledge about the system to be prognosed. The corresponding models are detailed. The proposed prognostic function contains graph traversal so its distribution is proposed to speed it up. It is carried out by generic agents

Failure Diagnosis and Prognosis of Safety Critical Systems: Applications in Aerospace Industries

Many safety-critical systems such as aircraft, space crafts, and large power plants are required to operate in a reliable and efficient working condition without any performance degradation. As a result, fault diagnosis and prognosis (FDP) is a research topic of great interest in these systems. FDP systems attempt to use historical and current data of a system, which are collected from various measurements to detect faults, diagnose the types of possible failures, predict and manage failures in advance. This thesis deals with FDP of safety-critical systems. For this purpose, two critical systems including a multifunctional spoiler (MFS) and hydro-control value system are considered, and some challenging issues from the FDP are investigated. This research work consists of three general directions, i.e., monitoring, failure diagnosis, and prognosis. The proposed FDP methods are based on data-driven and model-based approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the remaining useful life (RUL) of the faulty components accurately and efficiently. In this regard, two dierent methods are developed. A modular FDP method based on a divide and conquer strategy is presented for the MFS system. The modular structure contains three components:1) fault diagnosis unit, 2) failure parameter estimation unit and 3) RUL unit. The fault diagnosis unit identifies types of faults based on an integration of neural network (NN) method and discrete wavelet transform (DWT) technique. Failure parameter estimation unit observes the failure parameter via a distributed neural network. Afterward, the RUL of the system is predicted by an adaptive Bayesian method. In another work, an innovative data-driven FDP method is developed for hydro-control valve systems. The idea is to use redundancy in multi-sensor data information and enhance the performance of the FDP system. Therefore, a combination of a feature selection method and support vector machine (SVM) method is applied to select proper sensors for monitoring of the hydro-valve system and isolate types of fault. Then, adaptive neuro-fuzzy inference systems (ANFIS) method is used to estimate the failure path. Similarly, an online Bayesian algorithm is implemented for forecasting RUL. Model-based methods employ high-delity physics-based model of a system for prognosis task. In this thesis, a novel model-based approach based on an integrated extended Kalman lter (EKF) and Bayesian method is introduced for the MFS system. To monitor the MFS system, a residual estimation method using EKF is performed to capture the progress of the failure. Later, a transformation is utilized to obtain a new measure to estimate the degradation path (DP). Moreover, the recursive Bayesian algorithm is invoked to predict the RUL. Finally, relative accuracy (RA) measure is utilized to assess the performance of the proposed methods

Failure diagnosis and prognosis in stochastic discrete-event and cyber-physical systems

In this dissertation we study the problem of fault diagnosis in both discrete event systems and cyber physical systems. Discrete event systems (DESs) are event-driven systems with discrete states that evolve in response to abrupt occurrences of discrete changes (called events). The stochastic DESs are used to characterize the quantitative behavior of the system, by modeling the uncertainty on the occurrence of events as random variables with certain distribution. A stochastic DES is similar to the Markov chain models, with the difference being that, in stochastic DESs, the transition is labeled with the event while the event information is omitted in a Markov chain. Many physical systems, such as manufacturing systems, communication protocols, reactive software, telephone networks, traffic systems, robotics and digital hardware, can be modeled as DESs at a certain level of abstraction. Fault diagnosis is to detect the occurrence of a fault so as to enable any fault tolerant actions. It is a crucial and challenging problem that has attracted considerable attentions in the literature of software engineering, automotive systems, power systems and nuclear engineering. In this dissertation, we propose the online detection schemes for stochastic DESs and also introduce the notions of missed detections (MDs) and false alarms (FAs), or equivalently, false-negatives and false-positives, for the schemes. The idea is that given any observation (of partially observed events), the detector recursively computes the conditional probability of the nonoccurrence of a fault and issues a fault decision if the probability of the nonoccurrence of a fault falls below an appropriately chosen threshold, and issues no-decision otherwise. We establish that S-Diagnosability is a necessary and sufficient condition for achieving any desired levels of MD and FA rates, where the notion of S-Diagnosability was proposed by Thorsley, et al. in 2005, requiring that given any tolerable ambiguity level &rho and error bound &tau , there must exist a delay bound n such that for any fault trace, its extensions, longer than n and probability of ambiguity higher than &rho, occur with probability smaller than &tau . Algorithms for determining the detection scheme parameters of detection threshold and detection delay bound for the specified MD and FA rates requirement are also presented, based on the construction of an extended observer, which computes, for each observation sequence, the set of states reached in the system model, along with their probabilities and the number of post-fault transitions executed. This dissertation also studies the fault diagnosis in cyber physical systems, where the dynamics of the physical systems over discrete sample instances are described by stochastic difference equations, and the nonfault behaviors are specified by linear-time temporal logic (LTL) formulas over sequences of requirement variables that are functions of inputs and states (just as the outputs). We first introduce the notion of an input-output stochastic hybrid automaton (I/O-SHA), and then show that it can be used to model the refinement of a given discrete-time stochastic system against its LTL specification so as to identify the system behaviors that satisfy the nonfault specification versus the ones that violate it in form of reachability of a fault location. For this we propose a refinement algorithm that refines the system model in form of discrete-time stochastic equations with respect to its specification model in form of a Buchi acceptor, and the resulting refinement can be modeled as an I/O-SHA. We further show that the fault detection problem then reduces to a state estimation problem for the I/O-SHA. The performance of the detection protocol is evaluated in terms of its FA and MD rates. We additionally propose the notion of S-Diagnosability for I/O-SHA, which can guarantee the existence of detectors that can achieve any desired FA and MD rates. We further consider the fault prognosis problem, where the goal is to predict a fault prior to its occurrence, for stochastic DESs. We introduce m-steps Stochastic-Prognosability, or simply Sm-Prognosability, requiring for any tolerance level &rho and error bound &tau , there exists a reaction bound k &ge m, such that the set of fault traces for which a fault cannot be predicted k steps in advance with tolerance level &rho, occurs with probability smaller than &tau . Similar to the fault diagnosis problem, we formalize the notion of a prognoser that maps observations to decisions by comparing a suitable statistic with a threshold, and show that Sm-Prognosability is a necessary and sufficient condition for the existence of a prognoser with reaction bound at least m (i.e., prediction at least m-steps prior to the occurrence of a fault) that can achieve any specified FA and MD rate requirement. Moreover, we provide a polynomial algorithm for verifying Sm-Prognosability

Decision-making and problem-solving methods in automation technology

The state of the art in the automation of decision making and problem solving is reviewed. The information upon which the report is based was derived from literature searches, visits to university and government laboratories performing basic research in the area, and a 1980 Langley Research Center sponsored conferences on the subject. It is the contention of the authors that the technology in this area is being generated by research primarily in the three disciplines of Artificial Intelligence, Control Theory, and Operations Research. Under the assumption that the state of the art in decision making and problem solving is reflected in the problems being solved, specific problems and methods of their solution are often discussed to elucidate particular aspects of the subject. Synopses of the following major topic areas comprise most of the report: (1) detection and recognition; (2) planning; and scheduling; (3) learning; (4) theorem proving; (5) distributed systems; (6) knowledge bases; (7) search; (8) heuristics; and (9) evolutionary programming

A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms

A common problem in solar farms is to predict when accumulators stop working optimally and start losing efficiency. This paper proposes and describes how to use Bayesian networks together with expert systems to predict this moment by using a telecontrol multiagent system for monitoring solar farms with distributed sensors, which was developed in a previous work. To this end, a Bayesian network model and its implementation are proposed. The resulting system meets the requirements of telecontrol systems (reliability, flexibility, and response time), yields a solution for the prediction of lifespan batteries, and provides the multiagent system with autonomous intelligent capabilities and integrated learning.Consejería de Innovación, Ciencia y Empresas (Junta de Andalucía) P08-TIC-0386

Contrôle et diagnostic décentralisés des systèmes à évènements discrets approche multi-décisionnelle

De nos jours, les systèmes technologiques sont devenus très complexes (matériel informatique, logiciel, système de télécommunication, usine manufacturière, etc.), et cette complexité croît continuellement de sorte que les anciennes techniques intuitives utilisées pour leur conception, leur étude et leur réalisation deviennent inadaptées. À cause de cette complexité croissante, la probabilité pour qu'une erreur (ou panne) inattendue survienne est de plus en plus grande. Plus encore, quelques erreurs peuvent provoquer des accidents très graves causant des pertes économiques ou humaines. C'est dans ce cadre que les méthodes formelles ont été développées pour l'analyse, la conception et la réalisation des systèmes logiciels et électroniques quelque [i.e. quelle que] soit leur complexité. Ainsi, l'étude des systèmes à événements discrets (SED) a été introduite avec l'objectif de développer des méthodes formelles pour répondre à des besoins pressants, tels que le contrôle, le diagnostic, le pronostic, le test et la vérification des comportements discrets des systèmes technologiques. Cette thèse considère et généralise les études du contrôle et du diagnostic décentralisés des SED. Le principe commun du contrôle et du diagnostic décentralisés des SED est la prise de décision décentralisée, qui est basée sur l'utilisation d'une architecture décentralisée. Cette dernière est constituée de plusieurs décideurs locaux qui observent partiellement un SED et prennent des décisions locales qui sont ensuite fusionnées par un module de fusion D. Ce dernier, en se basant sur une fonction de fusion, calcule à partir des décisions locales une décision globale. Le système englobant les décideurs locaux et le module de fusion s'appelle un décideur décentralisé. L'ensemble de tous les décideurs décentralisés ayant D comme module de fusion est appelé D-architecture. La principale contribution de cette thèse est de proposer une nouvelle approche de prise de décision décentralisée, appelée multi-décision et qualifiée de multi-décisionnelle. Le principe de la multi-décision est basé sur l'utilisation de plusieurs (disons p) décideurs décentralisés (DD[indice supérieur j)[indice inférieur j=1,...,p] qui fonctionnent simultanément et en parallèle. Chaque DD[indice supérieur J] a une architecture décentralisée parmi celles qu'on trouve dans la littérature. C'est-à-dire que chaque DD[indice supérieur J] est constitué d'un ensemble de décideurs locaux ([Dec[indice supérieur J][indice inférieur i])[indice inférieur i=1,...,n] dont les décisions locales sont fusionnées par un module de fusion D[indice supérieur j] afin d'obtenir une décision globale. Dans l'architecture multi-décisionnelle, les décisions globales des p (DD[indice supérieur j])[indice inférieur j=1,...,p] sont fusionnées par un module D afin d'obtenir une décision effective qui respecte une propriété désirée Pr. L'intérêt de la multi-décision est que l'architecture ((DD[indice supérieur j])[indice inférieur j=1,..., p], D) constituée des différents (DD[indice supérieur j])[indice inférieur j =1,...,p] et de D généralise chacune des architectures DD[indice supérieur j]. C'est-à-dire que l'ensemble des SED auxquels on peut appliquer ((DD[indice supérieur j])[indice inférieur j=1,...,p], D) englobe les différents SED auxquels on peut appliquer les différents DD[indice supérieur j] séparément. Nous avons étudié l'approche multi-décisionnelle sur deux exemples de prise de décision : le contrôle supervisé et le diagnostic. On obtient alors le contrôle et le diagnostic multi-décisionnels. Dans les deux cas, l'approche multi-décisionnelle nécessite une décomposition de langages infinis (c.-à-d., contenant un nombre infini de séquences), qui est connue comme étant un problème difficile. Pour résoudre ce problème, on a proposé, dans le cas particulier des langages réguliers, une méthode qui transforme la décomposition d'un langage infini X en une décomposition d'un ensemble fini d'états marqués. Pour arriver à cela, on a dû s'imposer une restriction en ne considérant que les décompositions de X qui respectent une condition spécifique. Cette condition présente l'avantage de rendre les conditions d'existence de solutions vérifiables. Nous avons ainsi développé des algorithmes pour vérifier les conditions d'existence de solutions pour le contrôle et le diagnostic multi-décisionnels. Ces algorithmes ont le même ordre de complexité que les algorithmes qui vérifient les conditions d'existence de solutions pour le contrôle et le diagnostic décentralisés. Il est important de noter que les conditions d'existence obtenues pour une architecture multi-décisionnelle ((DD[indice supérieur j])[indice inférieur j=1,..., p], D) sont moins contraignantes que celles obtenues pour chacune des architectures DD[indice supérieur j]

Psychological Climate and Work Attitudes: The Importance of Telling the Right Story

In this field study, the authors explore how choosing one context over another influences both research results and implications. Using both quantitative and qualitative data, the authors examine context from both an organizational and a business-unit perspective by studying relationships between five psychological climate variables and outcomes of job satisfaction, affective commitment, and intent to leave. Results show different contextual influences between the organization and two business units, suggesting that different bundles of psychological climate variables yield similar outcomes depending on the context studied. These results bolster the contention that researchers need to identify the right context in field research