Keywords: Autonomous vehicles, Data driven methods, Fault detection and isolation
Abstract: This work proposes the development of a scheme for the fault diagnosis of the actuators of a simulated model accurately representing the behaviour of an autonomous underwater vehicle. The Fossen model usually adopted to describe the dynamics of the underwater vehicle has been generalised in this paper to take into account time--varying sea currents. The proposed fault detection and isolation strategy uses a data--driven approach relying on multi--layer perceptron neural networks that include auto--regressive exogenous prototypes. These tools are thus exploited to design a bank of dynamic neural networks for residual generation that are trained on the basis of the input and output measurements acquired from the simulator. The neural network bank is able to provide the detection of the faults affecting the actuators jointly with their isolation in case of simultaneous and concurrent faults The paper firstly describes the steps performed for deriving the proposed fault diagnosis solution. Secondly, the effectiveness of the scheme is demonstrated by means of high--fidelity simulations, in presence of faults and marine current.

Keywords: Fault detection and isolation, Data driven methods, Fault-forecasting methods
Abstract: This paper presents a fault diagnosis and prognosis based on an hybrid approach that combines structural and data-driven techniques. The proposed method involves two phases. Firstly, construct the structural model of the system using structural analysis method without using mathematical models (only the component description of the system), and find out its corresponding residuals in structural form. Secondly, perform the diagnosis and prognosis process, for which the analytical expressions of residuals are obtained from available historical data of the system operation. The diagnosis part consists in checking the evolution of residuals during the process, any inconsistency of residuals can be considered as a fault, so that the thresholds for each residual are introduced. The residuals are obtained using interval model that takes into account the uncertainty and noises affecting the system. Once the fault is detected, also it is possible to determine which fault occurred in the system using the FSM (Fault Signature Matrix) obtained from the structural analysis of the system and residual generation.. The prognosis part is developed by the same steps, but instead of considering the actual situation, it evaluates the tendency of deviation respect the nominal operation condition to predict the future residual inconsistency, allowing estimating the RUL (Remaining Useful Life) of the system. The interval model is also introduced for the future prediction of residuals, thus there will be an interval of RUL for each residual which contains the maximum and minimum RUL values. The proposed approach is applied to a brushless DC motor (BLDC) used as a case study. Simulation experiments illustrate the performance of the approach.

Keywords: Fault detection and isolation, Health monitoring, Discrete event and hybrid systems
Abstract: In this paper, the problem of fault diagnosis of an industrial water cooling system is addressed using a combined data-driven and model based approach. Using the energy balance equations, the design of the fault diagnosis system is based on structural analysis. As result of this analysis, a set of Minimally Structurally Overdetermine Sets (MSO) are obtained presenting the desired fault detectability and isolability properties. Since the mathematical expressions of such MSOs are very complex and highly non-linear, and there are an important number of parameters that should be estimated from data, a system identification approach based on machine learning techniques is used. Not only the nominal model but also the error model is estimated. Finally, the proposed approach is tested with the data obtained from a water cooling machine.

Keywords: Data driven methods, Fault detection and isolation, Power plants / energy transport
Abstract: The fault diagnosis of wind turbines includes extremely challenging aspects that motivate the research issues considered in this paper. In particular, this work studies fault diagnosis solutions that are considered in a viable way and used as advanced techniques for condition monitoring of dynamic processes. To this end, the work proposes the design of fault diagnosis techniques that exploits the estimation of the fault by means of data--driven approaches. To this end, the fuzzy and neural network structures are integrated with auto--regressive with exogenous input regressors, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. The capabilities of fault diagnosis schemes are validated by using a simulator of a wind turbine system. Moreover, at this stage the benchmark is also useful to analyse the robustness and the reliability characteristics of the developed tools in the presence of model--reality mismatch and modelling error effects featured by the wind turbine simulator. On the other hand, a hardware--in--the--loop tool is finally implemented for testing the performance of the developed fault diagnosis strategies in a more realistic environment.

Keywords: Fault detection and isolation, Health monitoring, Model-based methods
Abstract: A novel model-based approach for online condition monitoring of rolling element bearings based on efficient and robust numerical differentiation is proposed. The physical parameters may be identified using fault-free data. The main idea for fault detection is the design of signals, called residuals, sensitive only to faults using a simplified model of the bearing vibrations. These residuals are proportional to the force stemming from the faults and are easy to compute and implement. An approach for the design of a failure detection threshold is provided. This threshold can be designed to meet a desired probability of false alarms. The approach is validated using four experimental data sets stemming from different research centers.

Keywords: Design for reliability and safety, Data driven methods, Decision making
Abstract: Abnormalities in modern process industries are reported by alarms. Strong inter-connectivities within different units of a plant lead to annunciations of multiple alarms in a short period of time, which hinders a prompt operator response. A fundamental problem in such alarm floods is to identify the original source of abnormality by finding the directly affected alarm, known as the root cause. This is essentially a causal problem which has been tackled by many non-causal approaches, including transfer entropy and Bayesian network based methods, which despite their successful applications, have their own shortcomings. Causal identification requires intervention. That is, to ``intervene'' and manually change the distribution of a random variable A, and compare its conditional distribution with respect to another variable B before and during the intervention to examine whether A is caused by B. This acknowledged notion of causality, leads to causal Bayesian networks where unlike Bayesian networks, the edges are truly causal. Nevertheless, intervening the alarms during a fault event is implausible if not impossible, hindering the use of this rich notion of causality in alarm root cause analysis. We tackle this issue by treating abnormalities as uncertain interventions, because they, indeed, intervene the directly affected alarms and change their distributions. We then find the causal Bayesian network that best represents the alarm data. In addition to more-accurate root-cause identification, our causal Bayesian network based method has the ability to discover the root causes under multiple simultaneous abnormalities.

Keywords: Fault detection and isolation, Model-based methods, Civil engineering
Abstract: Leakages in Water Distribution Networks cause significant economical losses as well as environmental damage. Therefore, this paper proposes a novel leak location approach. The proposed method consists in using a modified differential evolution (DE) algorithm to solve the leak location task as an inverse problem based on the network’s hydraulic model. The modified DE algorithm considers the topological characteristics of the network. Temporal reasoning of the results is integrated with the proposal to improve the leak location performance. The strategy was tested under different uncertainty conditions with the model of the Modena water distribution network showing a performance ranging from 94 to 98 % leak location accuracy with reduced zone sizes compared to previous approaches based on data-driven classification tools.

Keywords: Model-based methods, Supervisory control
Abstract: This paper proposes a model predictive control strategy to optimize the management and reduce water losses due to leaks in water distribution systems (WDS). First, the mathematical model of the pipeline is presented. The model includes a possible leak and a pressure reducing valve (PRV), which is considered the actuator of the system. Second, to deal with the nonlinear behaviour of the PRV and constraints, a model predictive controller is considered to track the desired pressure profile at a critical node of the WDS. Simulations based on a realistic model of a WDS are presented to demonstrate the controller performance under two scenarios: leak-free and with a leak. The results show a considerable reduction in water losses that demonstrate the applicability of the method.

Keywords: Health monitoring, Civil engineering
Abstract: The assessment and prognosis of pipe life in water distribution networks has great potential in optimizing asset investment and protecting water resources. In the state-of-the-art, most of the research work about pipe life assessment focuses on revealing associated variables and regulations for the occurrence of pipe failures, which has scientific value but still far from assisting water industry directly in real operation. In order to provide a pipe life assessment and prognosis approach with practical significance, this paper presents: 1) a comparable approach to quantify impact of different factors (mainly age, material and diameter) on the occurrence of pipe failures using statistical reliability model based on cumulative Weibull distribution and survival model based on neural networks; 2) a prognosis method for the remaining useful life of pipes using the survival algorithm; 3) a maintenance and renewal plan of the network to assist daily operation of water operators by means of a checklist including risk levels (low, medium, high) under different factor ranges. The Barcelona water distribution network is used as a real life case study, demonstrating how the proposed approaches can be used.

Keywords: Water treatment, Fault detection and isolation, Health monitoring
Abstract: The paper presents an approach to the online estimation of the degree of degradation of the pump-pipeline assemblies of domestic or municipal low-power sewage pumping stations. The two types of faults are of concern: leakage and obliteration. The potential applicability is the main advantage of the proposed approach. It is because it does not require installing any additional instruments dedicated specially for diagnostic purposes. Moreover, it may be easily implemented in almost all programmable controllers. We calculate the diagnoses of pumping stations using heuristic knowledge, subjective logic, and timing of pumping station control cycles. Finally, we show the chosen results of simulations of faults in a low-power pumping station.

Keywords: Fault detection and isolation, Data driven methods
Abstract: Water scarcity is an issue that countries are facing worldwide and at the same time the water demand is further steadily increasing. With these conditions leakages in a water distribution cannot be afforded. In this paper, we provide a method for determining the location of leakages using the hydraulic properties of a network. To that end, we utilize a self-adaptive reduced order network model to generate residual vectors and compare these vectors to expected residual signatures for various leakage scenarios. The novelty of this works lies in the construction of these residual signatures which is by a pressure variation model. Apart from that, the self-adaptive reduced order model is seen necessary as the pressure variations due to leakages are small, and therefore, models for residual generation must have minimum plant-model mismatch. Tests on an EPANET model of a hydraulic network which is part of the water distribution network in Randers, Denmark is presented to demonstrate the localization method.

Keywords: Data driven methods, Fault detection and isolation, Decision making
Abstract: This paper presents a machine learning method for optimal pressure sensor placement in water distribution networks. The proposed approach considers annealing metaheuristics, and it is focused on the optimal placement of the sensors to perform leak localization. Unlike other works, this method considers a limited number of sensors to be placed and some restrictions on critical nodes that can be excluded or preselected. The approach is based on minimizing a cost function; this cost function is assigned as the leak location error, which varies depending on the subset of nodes where the sensors are assigned and the configuration of the leak location method. A leak localization technique based on k-NN classifiers was used, and during the minimization of the cost function, classifier hyperparameters were simultaneously optimized. The proposed method was tested on the Hanoi water distribution network programmed in MATLAB.

Keywords: Fault detection and isolation, Fault tolerant control / fault recovery, Aeronautics / aerospace
Abstract: This paper is an application oriented paper. It aims at demonstrating how model--based fault diagnosis and tolerant theories can be used to accommodate faults that may occur in spacecraft navigation units, i.e. at sensor level. The application support is the ESA's In Orbit Assembly (IOA) project. This project is undertaken with GMV Space and Thales Alenia Space industries. The goal of the IOA mission is to assembly autonomously a telescope on a halo orbit around the Earth-Moon L2 point. The proposed solution is based on the observer structure of the (discrete time) subspace predictor and a nonlinear observer, for fault diagnosis. For fault tolerance, the solution relies on multi-sensor data fusion techniques based on the extended Kalman filter updated from the inverse-covariance form of the Kalman filter. The solution is evaluated under a realistic industrial environment.

Keywords: Model-based methods, Aeronautics / aerospace, Design for reliability and safety
Abstract: This paper presents a novel set-based multi-sensor data fusion algorithm for combining aircraft 3D position estimates provided by three separate positioning systems: Inertial Reference System (IRS), Global Positioning System (GPS) and Instrument Landing System (ILS). An Extended Zonotopic Kalman Filter (EZKF) is proposed to solve the problem of IRS/GPS/ILS data fusion that rigorously encloses the nonlinearities of ILS measurement equations. Moreover, an adaptive tuning of the overall data fusion filter relies on a lower layer integrating a bank of elementary filters. The latters result from the simplification of first-order zonotopic Kalman filters optimizing a 1-norm accuracy criterion. Simulations using real flight data provided by Airbus illustrate the effectiveness of the proposed method.

Keywords: Aeronautics / aerospace, Health monitoring, Fault detection and isolation
Abstract: The reusability of a liquid propulsion rocket engine (LRPE) has gained tremendous attention in the recent years. The present paper deals with an automatic estimation of the Remaining Useful Life of a LPRE combustion chamber with the cracking of the internal wall due to the thermo-mechanical stress considered as the major degradation mode. The study is performed using simulated data generated by a fictive LRPE engine model and the approach developed in this worked is based on the Extended Kalman Filter. A hybrid approach is proposed for the failure prognostics by fusing the knowledge brought in by an approximately correct degradation model with actual sensor measurements. The RUL prediction is made with respect to a failure threshold set by the user. The study considers two kinds of missions: flight and ground missions. The results indicate the effectiveness of the approach under single as well as variable operating condition for LRPEs.

Keywords: Fault detection and isolation, Health monitoring, Aeronautics / aerospace
Abstract: This paper presents a simple and so easily implementable threshold and up-down counter-based voting algorithm for the fault detection of aircraft position information during landing. Easy implementation is inevitable in case of small unmanned aircraft due to the size weight and power consumption constraints. Most of the development is based-on real flight test data collected in frame of the VISION EU H2020 research project on a 4m wingspan aircraft. The aircraft’s position flight measurements were provided by an on-board GPS unit with SBAS correction and an on-board data-based inertial-barometric-camera sensor fusion ESKF algorithm. ILS was simulated as the aircraft and airport were not equipped with it. SBAS, ILS and ESKF errors were all artificially added to the nominal flight and simulated data. Pairwise comparison of position information is the basis of fault detection. Category I precision approach requirements are considered to tune and test the fault-detection algorithm first, by the selection of thresholds and then by the fine tuning of up-down counters to reduce false alarms. Both integrity risk and time-to-alert requirements are satisfied only the continuity risk resulted below the expected but this does not mean needless abortion of landings.

Keywords: Model-based methods, Aeronautics / aerospace
Abstract: This paper addresses the globally non - overshooting/ undershooting (NOUS) tracking control problem of linear-time invariant (LTI) multi-input/output (MIMO) systems by utilizing state feedback control with a minimal Frobenius norm feedback gain matrix. An algorithm is proposed that synthesizes such a minimal-norm feedback controller, which ensures the desired convergence rate in all the outputs of the closed-loop system. The key idea is to utilize the additional freedom arising from eigenvalue placement in a disk region in the complex plane characterized by a linear matrix inequality (LMI). A numerical example is presented to compare the effectiveness of the proposed algorithm with the existing approach.

Keywords: Data driven methods, Fault tolerant control / fault recovery, Power plants / energy transport
Abstract: To enhance both the safety and the efficiency of offshore wind park systems, faults must be accommodated in their earlier occurrence, in order to avoid costly unplanned maintenance. Therefore, this paper aims at implementing a fault tolerant control strategy by means of a data--driven approach relying on fuzzy logic. In particular, fuzzy modelling is considered here as it enables to approximate unknown nonlinear relations, while managing uncertain measurements and disturbance. On the other hand, the model of the fuzzy controller is directly estimated from the input--output signals acquired from the wind farm system, with fault tolerant capabilities. In general, the use of purely nonlinear relations and analytic methods would require more complex design tools. The design is therefore enhanced by the use of fuzzy model prototypes obtained via a data--driven approach, thus representing the key point if real--time solutions have to implement the proposed fault tolerant control strategy. Finally, a high--fidelity simulator including hardware--in--the-loop modules is exploited to validate the reliability and robustness characteristics of the developed methodologies also for on--line implementations.

Keywords: Data driven methods, Design for reliability and safety, Power plants / energy transport
Abstract: The interest towards renewable energy resources is increasing, and in particular it concerns wind and hydro powers, where the key point regards their efficient conversion into electric energy. To this end, control techniques can be used to meet this purpose, especially the ones relying on fuzzy models, due to their capabilities to manage nonlinear dynamic processes working in different conditions, and affected by measurement errors, uncertainty and disturbances. The design methods addressed in this paper were already developed and validated for wind turbine plants, and important results can be achieved from their appropriate design and application to hydroelectric plants. This is the key issue of the paper, which recalls some considerations on the proposed solutions, as well as their validation to these energy conversion systems. Note that works available in the related literature that consider both wind and hydraulic energy conversion systems investigate a limited number of common issues, thus leading to little exchange opportunities and reduced common research aspects. Another important point addressed in the paper is that the proposed control design solutions are able to take into account the different working conditions of these power plants. Moreover, uncertainty, disturbance and model--reality mismatch effects will be also considered when analyzing the reliability and robustness features of the derived control schemes. In fact, proper hardware--in--the--loop tools are included to simulate more realistic environments.

Keywords: Fault tolerant control / fault recovery, Health monitoring, Mechatronic and robotics
Abstract: The central objectives of the presented research work are the development of design methods and a control system which allow to incorporate knowledge concerning the health of system elements in industrial applications. The paper presents a framework combining fault-tolerant design, useful lifetime aware design, fault-tolerant control and planned condition dependent maintenance. These elements of the framework are explained and detailed investigations of some of these elements are presented. Fault-tolerant design is explained on the example of the drive modules of an industrial forklift. For useful lifetime aware design a detailed investigation concerning the health (respectively the remaining useful life (RUL)) of certain machine elements in forklifts in high storage warehouses is explained. The chosen machine elements are ball bearings due to their frequent application and enormous significance in industrial systems. For these elements, a multiple model-based Takagi-Sugeno scheme is presented that is capable to follow and predict the degradation of a technical system. Fault-tolerant control in this framework is explained through the application of a virtual fuzzy actuator for the drive system of the forklift.

Keywords: Fault tolerant control / fault recovery, Discrete event and hybrid systems, Health monitoring
Abstract: The paper proposes a practical framework for implementing a fault-tolerant scheduling of hybrid assembly systems which combine humans and automated technical systems. The crucial difficulty with this kind of systems is related to delays which may inevitably occur with human involvement. In this paper, such delays are treated as faults and suitably accommodated. The paper starts with a detailed overview of a case study being a battery assembly system. Subsequently, an intuitive modelling approach is used to describe the evolution of its state. Since the system contains a humans and automated technical systems, its is proposed to analyse the system with an innovative IoT platform called KIS.ME. As a result, a reference human performance model is obtained using a fuzzy logic approach. Having a human reference model, a predictive fault-tolerant scheduling is proposed along with suitable performance constraints. The final part of the paper shows the obtained results, which clearly exhibit potential benefits, which can be obtained by merging KIS.ME with a proposed scheduling approach.

Keywords: Fault tolerant control / fault recovery, Fault detection and isolation, Supervisory control
Abstract: The paper proposes a control scheme which is a combination of Fault-Tolerant Control and Iterative Learning Control. The main goal here is to compensate the effect of the actuator fault almost immediately. The control scheme is based on an observer, that estimates both the state and the actuator fault. Moreover, the design proposed guarantees the stability for the observer and the external disturbances influence is attenuated by application of the H infinity methodology. Finally, in order to evaluate the correctness and the performance of the designed scheme it is applied in Multi-tank system control scenario.

Keywords: Fault detection and isolation, Statistical and signal processing, Mechatronic and robotics
Abstract: In this paper, we aim at improving the change detection techniques by introducing an adaptive thresholding with a sliding time window. In particular, a real-time optimal sliding time window length is implemented without any preliminary learning step as required in conventional sensor-fault detection methods. Based on Shannon’s entropy, our method improves the change detection techniques using an adaptive thresholding. The technique can be applied by any change detection technique based on the generalized likelihood ratio (GLR). To validate the robustness of our approach, two commonly used change detection techniques are considered: the cumulative sum (Cusum) and the exponentially weighted Moving average (EWMA) control charts. Experimental validation is experimentally shown considering real data in the context of collaborative mobile robots. In addition, this experiment leads to a fault-tolerant fusion methodology based on the use of an extended Kalman filter (EKF).

Keywords: Fault detection and isolation, Model-based methods, Discrete event and hybrid systems
Abstract: An algorithm for synthesizing a diagnostic model of a distributed computing real-time system was proposed that is embedded in the system, runs in parallel with the main software of the system and allows simplifying the process of testing it. Sufficient conditions for observability and controllability are formulated for models in a field of real numbers.

Keywords: Fault tolerant control / fault recovery, Supervisory control
Abstract: Robust adaptive control (RAC) approaches have many state-of-the-art capabilities; however, they cannot provide near-real-time performance, especially for novel fault conditions. To address such issues, we introduce a framework called Intelligent Robust Adaptive Control (IRAC) for switched systems, which is based on randomized blending of controllers' actions. We show how our approach provides guarantees for stability, robustness, adaptivity, and near-real-time performance. We empirically compare a worst-case performance of our approach with other methods, using a ``hypothetical baseline" that takes some time for control inference but provides exact control outputs. This shows the impact on near-real-time control of the time for inference, and illustrates the benefits of IRAC.

Keywords: Fault tolerant control / fault recovery
Abstract: The paper is devoted to the problem of fault identification (reconstruction) in systems described by nonlinear models under the unmatched disturbances. A novel approach to construct sliding mode observer is suggested for systems which do not satisfy the general conditions required for fault identification: minimum phase, matching, and detectability conditions. The suggested approach is based on the reduced order model of the original system insensitive to the disturbances. This allows to reduce the dimension of sliding mode observer and relax the limitations imposed on the original system.

Keywords: Fault detection and isolation, Model-based methods
Abstract: The objectives of the paper are limitations in design of the interval unknown-input observer, predestined for the uncertain linear Metzler system with disturbances. Expanding the set of linear matrix inequalities warranting the strictly Metzler form of the interval unknown input observer system matrix, the paper presents sufficient condition to establish stable observer of the defined structure. It is shown that this problem can be solved through feasibility of given set of linear matrix inequalities. In contrast with the case of general uncertain linear continuous-time systems, some related properties are deduced to characterise interval unknown input Metzler observers and related residual filters.

Keywords: Autonomous vehicles
Abstract: This paper deals with the problem of robust longitudinal speed estimation for rail transportation system under wheel-adhesion related faults that corrupt speed measurements and lead to poor closed loop behavior. Two observers are used: the first one is a sliding mode filtering differentiator which gives an estimate of the adhesion force in finite-time. The second one is a continuous-discrete high-gain observer to estimate the longitudinal train speed from aperiodic measurements of the position beacons. It is shown that the speed estimation error is practically stable in spite of variable adhesion, measurement noise and aperiodically sampled position data. Simulations are provided to show the benefits of the proposed approach.

Keywords: Fault tolerant control / fault recovery, Fault detection and isolation, Model-based methods
Abstract: The intensified heat exchanger/reactor systems became very popular and interesting in the process intensification field. They combine both heat transfer and chemical reactions in one hybrid unit. Nevertheless, the supervision and the diagnosis of these systems is highly demanded in order to maintain their performance and ensure their safety. In this paper, a fault tolerant control system, based on a bank of adaptive observers with a backstepping-based control law, is employed to a new intensified heat exchanger/reactor, in order to detect, isolate and recover all possible dynamic faults.