Model-based fault diagnosis via structural analysis of a reverse osmosis plant

Water desalination is one approach to force water scarcity. One of the processes used for desalination is reverse osmosis. Like other systems, a reverse osmosis plant is susceptible to faults. A fault can lead to a loss of efficiency, or if the fault is severe to a total breakdown. Appropriate measures can minimize the impact of faults, but this requires in time fault detection. The following thesis shows a proposal for an online fault diagnosis system of a reverse osmosis plant. For the model-based approach, a mathematical model of a reverse osmosis plant has been developed. The model contains a new approach for modeling the interaction between the high-pressure pump, the brine valve, and the membrane module. Furthermore, six faults considered for fault diagnosis have been modeled. Two of the faults are plant faults: The leakage of the feed stream and membrane fouling. The other four faults are sensor or actuator malfunctions. The fault diagnosis system is developed via structural analysis, a graph-based approach to determine a mathematical model’s overdetermined systems of equations. With the structural analysis, 73 fault-driven minimal structurally overdetermined (FMSO) sets have been determined. The results show that all six faults are detectable. However, two faults are not isolable. Five of the FMSO sets have been chosen to deduce the residuals used for online fault detection and isolation. The simulations demonstrate that the calculated residuals are appropriate to detect and isolate the faults. If one assumes that only the considered faults occur, it is possible to determine some faults’ magnitude

Navigation System Fault Diagnosis for Underwater Vehicle

This paper demonstrates fault diagnosis on unmanned underwater vehicles (UUV) based on analysis of structure of the nonlinear dynamics. Residuals are generated using different approaches in structural analysis followed by statistical change detection. Hypothesis testing thresholds are made signal based to cope with non-ideal properties seen in real data. Detection of both sensor and thruster failures are demonstrated. Isolation is performed using the residual signature of detected faults and the change detection algorithm is used to assess severity of faults by estimating their magnitude. Numerical simulations and sea trial data show results with very favorable balance between detection and false alarm probabilities.(c) IFAC 2014. This is the authors' accepted and refereed manuscript to the article

Fault Diagnosis of Hybrid Systems with Dynamic Bayesian Networks and Hybrid Possible Conficts

Hybrid systems are very important in our society, we can find them in many engineering fields. They can develop a task by themselves or they can interact with people so they have to work in a nominal and safe state. Model-based Diagnosis (MBD) is a diagnosis branch that bases its decisions in models. This dissertation is placed in the MBD framework with Artificial Intelligence techniques, which is known as DX community. The kind of hybrid systems we focus on have a continuous behaviour commanded by discrete events. There are several works already done in the diagnosis of hybrid systems field. Most of them need to pre-enumerate all the possible modes in the system even if they are never visited during the process. To solve that problem, some authors have presented the Hybrid Bond Graph (HBG) modeling technique, that is an extension of Bond Graphs. HBGs do not need to enumerate all the system modes, they are built as the system visits them at run time. Regarding the faults that can appear in a hybrid system, they can be divided in two main groups: (1) Discrete faults, and (2) parametric or continuous faults. The discrete faults are related to the hybrid nature of the systems while the parametric or continuous faults appear as faults in the system parameters or in the sensors. Both types af faults have not been considered in a unified diagnosis architecture for hybrid systems. The diagnosis process can be divided in three main stages: Fault Detection, Fault Isolation and Fault Identification. Computing the set of Possible Conflicts (PCs) is a compilation technique used in MBD of continuous systems. They provide a decomposition of a system in subsystems with minimal analytical redundancy that makes the isolation process more efficient. They can be used for fault detection and isolation tasks by means of the Fault Signature Matrix (FSM). The FSM is a matrix that relates the different parameters (fault candidates) in a system and the PCs where they are used

Real-Time Fault Diagnosis of Permanent Magnet Synchronous Motor and Drive System

Permanent Magnet Synchronous Motors (PMSMs) have gained massive popularity in industrial applications such as electric vehicles, robotic systems, and offshore industries due to their merits of efficiency, power density, and controllability. PMSMs working in such applications are constantly exposed to electrical, thermal, and mechanical stresses, resulting in different faults such as electrical, mechanical, and magnetic faults. These faults may lead to efficiency reduction, excessive heat, and even catastrophic system breakdown if not diagnosed in time. Therefore, developing methods for real-time condition monitoring and detection of faults at early stages can substantially lower maintenance costs, downtime of the system, and productivity loss. In this dissertation, condition monitoring and detection of the three most common faults in PMSMs and drive systems, namely inter-turn short circuit, demagnetization, and sensor faults are studied. First, modeling and detection of inter-turn short circuit fault is investigated by proposing one FEM-based model, and one analytical model. In these two models, efforts are made to extract either fault indicators or adjustments for being used in combination with more complex detection methods. Subsequently, a systematic fault diagnosis of PMSM and drive system containing multiple faults based on structural analysis is presented. After implementing structural analysis and obtaining the redundant part of the PMSM and drive system, several sequential residuals are designed and implemented based on the fault terms that appear in each of the redundant sets to detect and isolate the studied faults which are applied at different time intervals. Finally, real-time detection of faults in PMSMs and drive systems by using a powerful statistical signal-processing detector such as generalized likelihood ratio test is investigated. By using generalized likelihood ratio test, a threshold was obtained based on choosing the probability of a false alarm and the probability of detection for each detector based on which decision was made to indicate the presence of the studied faults. To improve the detection and recovery delay time, a recursive cumulative GLRT with an adaptive threshold algorithm is implemented. As a result, a more processed fault indicator is achieved by this recursive algorithm that is compared to an arbitrary threshold, and a decision is made in real-time performance. The experimental results show that the statistical detector is able to efficiently detect all the unexpected faults in the presence of unknown noise and without experiencing any false alarm, proving the effectiveness of this diagnostic approach.publishedVersio

Sensor placement for fault diagnosis based on structural models: application to a fuel cell stak system

The present work aims to increase the diagnosis systems capabilities by choosing the location of sensors in the process. Therefore, appropriate sensor location will lead to better diagnosis performance and implementation easiness. The work is based on structural models ands some simplifications are considered in order to only focus on the sensor placement analysis. Several approaches are studied to solve the sensor placement problem. All of them find the optimal sensor configuration. The sensor placement techniques are applied to a fuel cell stack system. The model used to describe the behaviour of this system consists of non-linear equations. Furthermore, there are 30 candidate sensors to improve the diagnosis specifications. The results obtained from this case study are used to strength the applicability of the proposed approaches.El present treball té per objectiu incrementar les prestacions dels diagnosticadors mitjançant la localització de sensors en el procés. D'aquesta manera, instal·lant els sensors apropiats s'obtenen millors diagnosticador i més facilitats d'implementació. El treball està basat en models estructurals i contempla una sèrie de simplificacions per tal de entrar-se només en la problemàtica de la localització de sensors. S'utilitzen diversos enfocs per tal de resoldre la localització de sensors, tot ells tenen com objectiu trobar la configuració òptima de sensors. Les tècniques de localització de sensors són aplicades a un sistema basat en una pila de combustible. El model d'aquest sistema està format per equacions no lineals. A més, hi ha la possibilitat d'instal·lar fins a 30 sensors per tal de millorar la diagnosis del sistema. Degut a aquestes característiques del sistema i del model, els resultats obtinguts mitjançant aquest cas d'estudi reafirmen l'aplicabilitat dels mètodes proposats.Postprint (published version

Incident detection and isolation in drilling using analytical redundancy relations

Early diagnosis of incidents that could delay or endanger a drilling operation for oil or gas is essential to limit field development costs. Warnings about downhole incidents should come early enough to allow intervention before it develops to a threat, but this is difficult, since false alarms must be avoided. This paper employs model-based diagnosis using analytical redundancy relations to obtain residuals which are affected differently by the different incidents. Residuals are found to be non-Gaussian - they follow a multivariate $t$-distribution - hence, a dedicated generalized likelihood ratio test is applied for change detection. Data from a 1400 meter horizontal flow loop test facility is used to assess the diagnosis method. Diagnosis properties of the method are investigated assuming either with available downhole pressure sensors through wired drill pipe or with only topside measurements available. In the latter case, isolation capability is shown to be reduced to group-wise isolation, but the method would still detect all serious events with the prescribed false alarm probability

Temperature-Driven Anomaly Detection Methods for Structural Health Monitoring

Reported in this thesis is a data-driven anomaly detection method for structural health monitoring which is based on the utilization of temperature-induced variations. Structural anomaly detection should be able to identify meaningful changes in measurements which are due to structural abnormal behaviour. Because, the temperature-induced variations and structural abnormalities may produce significant misinterpretations, the development of solutions to identify a structural anomaly, accounting for temperature influence, from measurements, is a critical procedure to support structural maintenance. A temperature-driven anomaly detection method is proposed, that introduces the idea of blind source separation for extracting thermal response and for further anomaly detection. Two thermal feature extraction methods are employed corresponding to the classification of underdetermined and overdetermined methods. The underdetermined method has the three phases of: (a) mode decomposition by utilising Empirical Mode Decomposition or Ensemble Empirical Mode Decomposition; (b) data reduction by performing Principal Component Analysis (PCA); (c) blind separation by applying Independent Component Analysis (ICA). The overdetermined method has the two stages of the pre-indication according to PCA and the blind separation by the devotion of ICA. Based on the extracted thermal response, the temperature-driven anomaly detection method is later developed in combination with the four methodologies of: Moving Principal Component Analysis (MPCA); Robust Regression Analysis (RRA); One-Class Support Vector Machine (OCSVM); Artificial Neural Network (ANN). Therefore, the proposed temperature-driven anomaly detection methods are designed as Td-MPCA, Td-RRA, Td-OCSVM, and Td-ANN. The proposed thermal feature extraction methods and temperature-driven anomaly detection methods have been investigated in the context of three case studies. The first case is a numerical truss bridge with simulated material stiffness reduction to create levels of damage. The second case is a purpose constructed truss bridge in the Structures Lab at the University of Warwick. The third case study is Ricciolo curved viaduct in Switzerland. Two primary findings can be confirmed from the evaluation results of these three case studies. Firstly, temperature-induced variations can conceal damage information in measurements. Secondly, the detection abilities of temperature-driven methods, which are Td-MPCA, Td-RRA, Td-OCSVM, and Td-ANN, for disclosing slight anomalies in time are more efficient when compared with the current anomaly detection method, which are MPCA, RRA, OCSVM, and ANN. The unique features of the author’s proposed temperature-driven anomaly detection method can be highlighted as follows: (a) it is a data-driven method for extracting features from an unknown structural system. In another word, the prior knowledge of the structural in-service conditions and physical models are not necessary; (b) it is the first time that blind source separation approaches and relative algorithms have been successfully employed for extracting temperature-induced responses; (c) it is a new approach to reliably assess the capability of using temperature-induced responses for anomaly detection

Monitoring, fault detection and estimation in processes using multivariate statistical

Multivariate statistical techniques are one of the most widely used approaches in data driven monitoring and fault detection schemes in industrial processes. Concretely, principal component analysis (PCA) has been applied to many complex systems with good results. Nevertheless, the PCA-based fault detection and isolation approaches present some problems in the monitoring of processes with different operating modes and in the identification of the fault root in the fault isolation phase. PCA uses historical databases to build empirical models. The models obtained are able to describe the system¿s trend. PCA models extract useful information from the historical data. This extraction is based on the calculation of the relationship between the measured variables. When a fault appears, it can change the covariance structure captured, and this situation can be detected using different control charts. Another widely used multivariate statistical technique is partial least squares regression (PLS). PLS has also been applied as a data driven fault detection and isolation method. Moreover, this type of methods has been used as estimation techniques in soft sensor design. PLS is a regression method based on principal components. The main goal of this Thesis deals with the monitoring, fault detection and isolation and estimation methods in processes based on multivariate statistical techniques such as principal component analysis and partial least squares. The main contributions of this work can be arranged in the three following topics: ¿ The first topic is related with the monitoring of continuous processes. When a process operates in several operating modes, the classical PCA approach is not the most suitable method. In this work, an approach for monitoring the whole behaviour of a process, taking into account the different operating modes and transient states, is presented. The monitoring of transient states and start-ups is studied in detail. Also, the continuous processes which do not operate in a strict steady state are monitored in a similar way to the transient states. ¿ The second topic is related with the combination of model-based structural model decomposition techniques and principal component analysis. Concretely, the possible conflicts (PCs) approach is applied. PCs compute subsystems within a system model as minimal subsets of equations with an analytical redundancy property to detect and isolate faults. The residuals obtained with this method can be useful to perform a complete fault isolation procedure. These residuals are monitored using a PCA model in order to simplify and improve the fault detection task. ¿ The third topic addresses the estimation task in soft sensor design. In this case, the soft sensors of a real process are studied and improved using neural networks and multivariate statistical techniques. In this case, a dry substance (DS) content sensor based on indirect measurements is replaced by a neural network-based sensor. This type of sensors take into account more variables of the process and obtain more robust and accurate estimations. Moreover, this sensor can be improved using a PCA layer at the network input in order to reduce the number of inputs in the network. Also, a PLS-based sensor is designed in this topic. It also improves the sensor based on indirect measurements. Finally, the different approaches developed in this work have been applied to several process plants. Concretely, a two-communicated tanks system, the evaporation section of a sugar factory and a reverse osmosis desalination plant are the systems used in this dissertationDepartamento de Ingenieria de Sistemas y Automátic

Algorithms for Fault Detection and Diagnosis

Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions

34th Midwest Symposium on Circuits and Systems-Final Program

Organized by the Naval Postgraduate School Monterey California. Cosponsored by the IEEE Circuits and Systems Society. Symposium Organizing Committee: General Chairman-Sherif Michael, Technical Program-Roberto Cristi, Publications-Michael Soderstrand, Special Sessions- Charles W. Therrien, Publicity: Jeffrey Burl, Finance: Ralph Hippenstiel, and Local Arrangements: Barbara Cristi