Model-based fault detection and isolation for a gas–liquid separation unit

A multi-engine cluster of a launcher operates in extreme conditions, making the tasks of detection and isolation complex, with only a precise group of faults with small magnitudes worth being detected and isolated. To address this, a model-based Fault Detection and Isolation (FDI) system is developed for a multi-engine cluster using Structural Analysis (SA). A model of a multi-engine cluster composed of three engines is established, where different sensor and actuator faults are considered. After analyzing the model using SA, more than 16,000 residual generator candidates are obtained. An algorithm is then proposed to find subsets of residuals with minimal cardinality capable of isolating all faults. Each subset generates a unique fault signature for each fault, enabling fault isolation. The selection of the best subsets is based on the new definition of the Subset Sensitivity Index (SSI), according to which the subsets composed of the most sensitive residuals have the highest SSI values. The residual sensitivity is calculated using a new method based on the signal-to-noise ratio (SNR), where a new definition of the Residual Sensitivity Index (RSI) is given. Furthermore, a new algorithm is presented for fault isolation. Suppose a theoretical fault signature is not observed. In that case, the algorithm uses the knowledge of the residual sensitivity (RSI) to define which residual did not behave as expected and predicts which fault is active in the system. Monte Carlo simulations are conducted under ten different faults to analyze the performance of the proposed FDI system, including actuators and sensor faults.

Model-based fault detection and isolation (FDI) for an overactuated mechatronic vehicle is presented. The linear single-track model is extend to reflect the layout of the overactuated vehicle as well as its longitudinal dynamics, and sensor and actuator faults are added into the model. The DLR Fault Detection Toolbox, which makes use of rational nullspace bases computation to design residual generators, is used for the systematic design of structured residuals. A minimum set of residuals is selected according to their robustness and ability to isolate faults. The fault detection and isolation system is validated in a simulation in connection with a double track model using the parameters of the ROboMObil prototype vehicle.

This paper covers model-based fault detection and isolation for linear and nonlinear distributed parameter systems (DPS). The first part mainly deals with actuator, sensor and state fault detection and isolation for a class of DPS represented by a set of coupled linear partial differential equations (PDE). A filter based observer is designed based on the linear PDE representation using which a detection residual is generated. A fault is detected when the magnitude of the detection residual exceeds a detection threshold. Upon detection, several isolation estimators are designed using filters whose output residuals are compared with predefined isolation thresholds. A fault on a linear DPS is declared to be of certain type if the corresponding isolation estimator output residual is below its isolation threshold while the other fault isolation estimator output residual is above its threshold. Next, the fault location is determined when a state fault is identified. The second part of this paper focuses on fault detection and isolation of nonlinear DPS by using a Luenberger type observer. Here fault isolation framework is introduced to isolate actuator, sensor and state faults with isolability condition by using additional boundary measurements and without filters. Finally, the effectiveness of the proposed fault detection and isolation schemes for both linear and nonlinear DPS are demonstrated through simulation.

SummaryThis paper proposes an integrated actuator and sensor active fault‐tolerant model predictive control scheme. In this scheme, fault detection is implemented by using a set‐valued observer, fault isolation (FI) is performed by set manipulations, and fault‐tolerant control is carried out through the design of a robust model predictive control law. In this paper, a set‐valued observer is used to passively complete the fault detection task, while FI is actively performed by making use of the constraint‐handling capability of robust model predictive control. The set‐valued observer is chosen to implement fault detection and isolation (FDI) because of its simple mathematical structure that is not affected by the type of faults such as sensor, actuator, and system‐structural faults. This means that only one set‐valued observer is needed to monitor all considered actuator and sensor statuses (health and fault) and to carry out the fault detection and isolation task instead of using a bank of observers (each observer matching a health/fault status). Furthermore, in the proposed scheme, the advantage of robust model predictive control is that it can effectively deal with system constraints, disturbances, and noises and allow to implement an active FI strategy, which can improve FI sensitivity when compared with the passive FI methods. Finally, a case study based on the well‐known two‐tank system is used to illustrate the effectiveness of the proposed fault‐tolerant model predictive control scheme. Copyright © 2016 John Wiley & Sons, Ltd.

The purpose of this paper is mainly aimed to provide an energy security strategy for the petroleum production and processing in the grand challenges. Fault detection and diagnosis is the central component of abnormal event management (AEM) [1]. According to the International Federation of Automatic Control (IFAC), a fault is defined as an unpermitted deviation of at least one characteristic property or parameter of the system from the acceptable/usual/standard condition [2-4]. Generally, there are three parts in a fault diagnosis system, detection, isolation, and identification [5, 6, 7]. Depending on the performance of fault diagnosis systems, they are called FD (for fault detection) or FDI (for fault detection and isolation) or FDIA (for fault detection, isolation and analysis) systems [5]. As the increasing needs for energy grows rapidly, energy security becomes an important issue especially in petroleum production and processing. The importance can be mainly considered in following perspectives: higher system performance, product quality, human safety, and cost efficiency [5, 8]. With this in mind, the purpose of this research is to develop a Fault Detection and Isolation (FDI) system which is capable to diagnosis multiple sensor faults in nonlinear cases. In order to lead this study closer to real world applications in oil industries, the system parameters of the applied system are assumed to be unknown. In the first step of the proposed method, phase space reconstruction techniques are used to reconstruct the phase space of the applied system. This step is aimed to infer the system property by the collected sensor measurements. The second step is to use the reconstructed phase space to predict future sensor measurements, and residual signals are generated by comparing the actually measured measurements to the predicted measurements. Since, in practice, residual signals will not perfectly equal to zero in the fault-free situation, Multiple Hypothesis Shiryayev Sequential Probability Test (MHSSPT) is introduced to further process those residual signals, and the diagnostic results are presented in probability. In addition, the proposed method is extended to a non-stationary case by using the conservation/dissipation property in phase space. The proposed method is examined by both of simulated data and real process data. The three tank model is modeled according to a nonlinear laboratory setup DTS200 and introduced for generating simulated data. On the other hand, the real process data collected from a sugar factory actuator system are also used to examine the proposed method. According to our results obtained from simulations and experiments, the proposed method is capable to indicate both of healthy and faulty situations. In the end, we have to emphasize that the proposed approach is not limited in the applications of petroleum production and processing. For example, our approach can also apply to enhance the quality of water and avoid the discharges, such as leakage, in the process of water resource management. Therefore, the proposed approach not only benefits the issue of energy security but also other issues in the grand challenges.

A fault detection and isolation (FDI) system is presented that can detect and isolate nuclear power plant (NPP) faults occurring in interacting systems. The proposed methodology combines two tools, observer-based residual generation and neural network pattern matching, into a powerful, hybrid diagnostic system. A computer-based model of a commercial boiling water reactor (BWR) is used as the reference plant.Two FDI methods are implemented on each of two BWR systems, and their performance characteristics are compared. One method uses conventional neural network techniques that use parameter values for input, and a second, hybrid methodology uses system models to create residuals for input to a neural network. Both FDI systems show good generalization abilities, but only the hybrid system decouples system interactions. Although implementation is impractical for all NPP systems, this hybrid technique is most useful in specific applications where operators have difficulty diagnosing faults in strongly interacting systems.

In this paper, it is shown that the high automation level of the object-oriented modeling paradigm for physical systems can significantly rationalize the design procedure of fault detection and isolation (FDI) systems. Consequently, an object-oriented FDI method for complex engineering systems consisting of subsystems from different physical domains like mechatronic systems, commercial vehicles, and chemical process plants is developed. The mathematical composition of the objects corresponding to the subsystems results in a differential algebraic equation (DAE) that describes the overall system. This DAE is automatically analyzed and transferred into a set of residual generators that enable a two-stage FDI procedure for multiple fault modes.

<div class="htmlview paragraph">The stochastic Fault Detection and Isolation (FDI) algorithm, known as the statistical local approach, is applied in a model-based framework to the diagnosis of component faults in the air-intake system of an automotive engine. The FDI scheme is first presented as a general methodology that permits the detection of faults in complex nonlinear systems without the need for building inverse models or numerous observers. Although sensor and actuator faults can be detected by this FDI methodology, component faults are generally more difficult to diagnose. Hence, this paper focuses on the detection and isolation of component faults for which the local approach is especially suitable. The challenge is to provide robust on-board diagnostics regardless of the inherent nonlinearities in a system and the random noise present. In particular, the local approach is shown to simplify and reduce the complicated FDI problem to the standard problem of detecting changes in the mean value of a Gaussian vector with a constant covariance matrix. In doing so, the least square score is implemented as our primary residual, and the statistical properties of the so-called improved residuals are used to detect changes in the parameters of a parametric model. Applying the local approach, in both offline and online configurations, to a mean-value model of a spark-ignition engine, component faults in the air-intake system, namely small abrupt changes in the volumetric efficiency coefficient <i>η<sub>o</sub></i> and the throttle discharge coefficient <i>C<sub>d</sub></i>, have been successfully detected and isolated. The FDI methodology shows robustness to modeling errors and noise, proving the fact that a perfect model is not required to monitor a given system. Since unknown plant parameters are usually determined by identification techniques, which never yield perfect results, modeling errors are indeed expected in practice. Consequently, the local approach sheds promises for robust on-board diagnostics in the face of the overall nonlinear powertrain problem.</div>

A combined data‐driven and observer‐design methodology for fault detection and isolation (FDI) in hybrid process systems with switching operating modes is proposed. The main contribution is to construct a unified framework for FDI by integrating Gaussian mixture models (GMM), subspace model identification (SMI), and results from unknown input observer (UIO) theory. Initially, a GMM is built to identify and describe the multimodality of hybrid systems using the recorded input/output process data. A state‐space model is then obtained for each specific operating mode based on SMI if the system matrices are unknown. An UIO is designed to estimate the system states robustly, based on which the fault detection is laid out through a multivariate analysis of the residuals. Finally, by designing a set of unknown input matrices for specific fault scenarios, fault isolation is performed through the disturbance‐decoupling principle from the UIO theory. A significant benefit of the developed framework is to overcome some of the limitations associated with individual model‐based and data‐based approaches in dealing with the problem of FDI in hybrid systems. Finally, the validity and effectiveness of the proposed monitoring framework are demonstrated using a numerical example, a simulated continuous stirred tank heater process, and the Tennessee Eastman benchmark process. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2805–2814, 2014

Due to the growing demand on high efficient heat ventilation and air conditioning (HVAC) systems, how to improve the efficiency of HVAC system regarding reduces energy consumption of system has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependent upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the fault detection and isolation (FDI) system plays a crucial role for identifying failures. Finding healthy HVAC source as the reference for health monitoring is the main aim in this area. To dispel this concern a comprehensive transient model of heat ventilation and air conditioning (HVAC) systems is developed in this study. The transient model equations can be solved efficiently using MATLAB coding and simulation technique. Our proposed model is validated against real HVAC system regarding different parts of HVAC. The developed model in this study can be used for a pre tuning of control system and put to good use for fault detection and isolation in order to accomplish high-quality health monitoring and result in energy saving. Fan supply consider as faulty device of HVAC system with six fault type. A sensitivity analysis based on evaluated model shows us three features are sensitive to all faults type and three auxiliary features are sensitive to some faults. The magnitude and trait of features are a good potential for automatic fault tolerant system based on machine learning systems

With the increased availability of condition monitoring data on the one hand and the increased complexity of explicit system physics-based models on the other hand, the application of data-driven approaches for fault detection and isolation has recently grown. While detection accuracy of such approaches is generally very good, their performance on fault isolation often suffers from the fact that fault conditions affect a large portion of the measured signals thereby masking the fault source. To overcome this limitation, we propose a hybrid approach combining physical performance models with deep learning algorithms. Unobserved process variables are inferred with a physics-based performance model to enhance the input space of a data-driven diagnostics model. The resulting increased input space gains representation power enabling more accurate fault detection and isolation. To validate the effectiveness of the proposed method, we generate a condition monitoring dataset of an advanced gas turbine during flight conditions under healthy and four faulty operative conditions based on the Aero-Propulsion System Simulation (C-MAPSS) dynamical model. We evaluate the performance of the proposed hybrid methodology in combination with two different deep learning algorithms: deep feed forward neural networks and Variational Autoencoders, both of which demonstrate a significant improvement when applied within the hybrid fault detection and diagnostics framework. The proposed method is able to outperform pure data-driven solutions, particularly for systems with a high variability of operating conditions. It provides superior results both for fault detection as well as for fault isolation. For the fault isolation task, it overcomes the smearing effect that is commonly observed in pure data-driven approaches and enables a precise isolation of the affected signal. We also demonstrate that deep learning algorithms provide a better performance on the fault detection task compared to the traditional machine learning algorithms.

This paper deals with the problem of fault detection and isolation (FDI) of a networked control system (NCS). In this configuration a linear time invariant(LTI) system communicates with controller/FDI over a shared medium. The medium supports a limited number of simultaneous connections between sensors and FDI system. Furthermore, it is assumed that there is the random and unknown packet dropouts in the controller-to-actuator link. Objective is to find a strategy for residual signals generating which network effects are taken into account. In addition, by means of these residuals fault detection and isolation of system is possible. A set of structured residual based on parity relation approach is proposed. It guarantees robustness to system disturbances and packet dropout while each residual is designed to be sensitive to some sensor faults. The efficiency of the proposed approach will be illustrated with a simulation example.

The air and fuel management of modern diesel engines is based on a feedforward control approach. As a consequence, faults of the air or the fuel path have a direct impact on the emissions that cannot be observed nor compensated. Faults due to the ageing of engine parts are very common; thus engines are designed with tolerances large enough to fulfil the emission standards for their entire lifetime. This paper presents a method to locate and quantify engine faults due to the ageing of parts of modern diesel engines in order to help to reduce design tolerances. A relative air-to-fuel ratio and a nitrogen oxides emission sensor have been applied to a test-bench engine, and control-oriented emission models have been developed. Based thereon, a model-based fault detection and isolation system has been implemented. The sensor signals are compared with the models in order to detect a possible fault, and fault location and size are estimated by means of a bank of extended Kalman filters, one for each fault considered. Results from the New European Driving Cycle show that the developed control-oriented models are accurate, and the faults can be correctly detected and isolated.

Degradation monitoring is of paramount importance to safety and reliability of aircraft operations and also for timely maintenance of its critical components. This two-part paper formulates and validates a novel methodology of degradation monitoring of aircraft gas turbine engines with emphasis on detection and isolation of incipient faults. In a complex system with multiple interconnected components (e.g. an aircraft engine), fault isolation becomes a crucial task because of possible input—output and feedback interactions among the individual components. This paper, which is the first of two parts, presents the underlying concepts of fault detection and isolation (FDI) in complex dynamical systems. The FDI algorithms are formulated in the setting of symbolic dynamic filtering (SDF) that has been recently reported in literature. The underlying concept of SDF is built upon the principles of symbolic dynamics, statistical pattern recognition, and information theory. In addition to abrupt large faults, the SDF-based algorithms are capable of detecting slowly evolving anomalies (i.e. deviations from the nominal behaviour) based on analysis of time series data of critical process variables of different engine components. The second part, which is a companion paper, validates the concept, laid out in the first part, on the simulation test bed of a generic two-spool turbofan aircraft engine model for detection and isolation of incipient faults.

Although Fault Detection and Isolation systems have been widely studied in recent years, it is still a very active research field due to its relevance in industrial production systems. In this paper, a new approach for multiple fault detection by using residual evaluation is proposed. First, an analytical redundancy scheme for residual generation is applied using nonlinear autoregressive networks with exogenousinputs for normal and faulty conditions. Simultaneous fault data is included in the training set in order to ensure multiple fault detection.Then, an adaptive filter considering statistic measures from input is used to increase sensibility and robustness. Filter coefficients are obtained off-line through genetic algorithm optimization. Finally, a neural network classifier is used for fault isolation. The proposed algorithm is tested on a rotary mechatronic test bench for backlash, bearing static friction and transmission faults to show the effectiveness of the proposed detection.