Model-based Fault Detection Research Articles

Intelligent faults diagnostics of turbine vibration’s via Fourier transform and neuro-fuzzy systems with wavelets exploitation

ABSTRACT Gas turbines play a vital role in gas transportation and power generation, but they are prone to instability phenomena that can lead to vibrations, shorten equipment lifespan, and result in catastrophic failures. To tackle these challenges, a paper introduces an integrated approach that leverages advanced techniques like Fourier transform, Neuro-Fuzzy systems, and wavelet analysis for continuous monitoring of the MS5002C turbine’s condition. The proposed method begins by collecting operational data and utilizing the Fourier transform to measure vibratory quantities, accurately representing their evolution through spectral data obtained from the analyzed signals. Adaptive inference-based algorithms of neuro-fuzzy systems are then employed to generate turbine failure indicators. This approach enables the development of a model-based fault detection method that compares the actual turbine operation with the estimated operation derived from a pre-established model, enabling the classification of detected faults. To enhance decision-making quality, evaluation, and validation of the diagnostic strategy’s performance, a multi-resolution analysis based on the wavelet transform is applied. The presented results from various implementation and validation tests demonstrate the effectiveness of this intelligent diagnostic approach in detecting and analyzing gas turbine vibrations. The paper exhibits promising outcomes in real-time monitoring, ensuring the operational safety of the turbine.

A comparative investigation between rule- and inverse model-based fault detection and diagnostics for HVAC control systems

Fault detection and diagnostics (FDD) tools provide valuable information regarding system faults and deviation from expected operation. Most existing FDD tools apply rule-based fault detection algorithms that generate an alarm when a rule is met; however, these tools cannot evaluate the overall performance of a system. Inverse-model-based FDD algorithms can be deployed to complement the fault alarms triggered by rule-based building energy management systems (BEMS). This paper examines the faults detected by rule- and inverse model-based algorithms used to detect faults in multiple zone variable air volume air handling unit systems. The capability of the rule- and inverse model-based algorithms in detecting and diagnosing faults is demonstrated through illustrative examples using data from three commercial buildings in New Brunswick, Canada. The results show that inverse model-based algorithms could diagnose faults that were not detected by the rule-based FDD algorithms implemented in a commercially available BEMS tool.

Twin model-based fault detection and tolerance approach for in-core self-powered neutron detectors

Twin model-based fault detection and tolerance approach for in-core self-powered neutron detectors

Evolving fuzzy prediction interval for fault detection in a heat exchanger

The objective of this work is to design a novel evolving fuzzy prediction interval for modeling a nonlinear system and to implement a novel interval-based algorithm for fault detection. To achieve these goals, three frameworks are studied to provide the basis for the ideas to be proposed. First, the prediction intervals developed in the literature to characterize the uncertainties in modeling systems. Second, the evolving intelligence systems presented in the literature to improve the predictive models through adaptability to temporal changes. Third, the model-based error detection algorithms that detect anomalies in system behavior based on the error of the models. This work proposes a complete recursive design of the evolving fuzzy prediction interval based on interval coverage estimation. This estimation is based on the accumulated number of past time points in which the interval contained the measurements of the modeled system. Additionally, a new interval-based fault detection algorithm is proposed in this work that determines the activation of alarms based on the increment of the proposed interval failure index. This novel index is similar in structure to the interval coverage estimation. The proposed recursively evolving fuzzy prediction interval and interval-based fault detection algorithm were tested on a heat exchanger system whose characteristics were affected by external changes. The experimental results show the effectiveness of the proposed interval model in representing the dynamics of the heat exchanger. Furthermore, the experiment shows that the proposed fault detection algorithm achieves comparable accurate performance to the results of a previous model-based fault detection method.

LPV Model-Based Fault Detection and Isolation in DC Microgrids Through Signature Recognition

LPV Model-Based Fault Detection and Isolation in DC Microgrids Through Signature Recognition

Fault detection and isolation for a secondary loop refrigeration system

Secondary loop refrigeration systems offer advantages over conventional system architectures: refrigerant charge reduction, maintenance simplification, and reduced safety hazards. Currently, no model-based fault detection for a secondary loop system has been reported. This article presents a fault detection and isolation scheme for a secondary loop refrigeration system where parametric faults (dirt/ice-buildup and secondary loop pump failure) and sensor faults (four temperature sensors in the secondary loop) are detected and isolated. The fault detection and isolation system is based on unknown input observers and on extended Kalman filters. The models are obtained through physical relations combined with experimental parameter determination (gray-box model) and are cross-validated against measurement data. The complete fault detection and isolation architecture is experimentally validated on a secondary loop refrigeration system mounted on a cooling box inside a climate chamber. Experimental results show that important parametric faults can be reliably detected and isolated, and the fault detection and isolation of sensor faults are effective.

Filter-Based Fault Detection and Isolation in Distributed Parameter Systems Modeled by Parabolic Partial Differential Equations

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.

An Integrated Model-Based and Data-Driven Gap Metric Method for Fault Detection and Isolation.

This article proposes an integrated approach of model-based and data-driven gap metric fault detection and isolation in a stochastic framework. For actuator and sensor faults, an adaptive Kalman filter combining with the generalized likelihood ratio method is suggested. For component faults, especially incipient faults, the model-based scheme maybe not a good choice due to the existence of disturbances or noises. Hence, a novel data-driven gap metric strategy is presented. The design of the appropriate fault cluster center model and radius via the gap metric technique is put forward to enhance the isolability of the incipient faults. Numerical simulation results are given to demonstrate the effectiveness of the proposed fault detection and isolation algorithm.

Modeling, fault detection, and stabilization of quadrotor unmanned aerial vehicle with rotor thrust deviation fault

This study deals with the thrust deviation of quadrotors during a flight accident. This is a novel structural fault wholly ignored in previous studies, and its kinematic and dynamic equations are developed using the Newton law and differentiating the angular momentum equation. The thrust deviation fault has been detected using the governing model of the faulty system, Thau observer, and calculation of residual value. For evaluation of the performance of the proposed fault detection method, it is assumed that limited disturbances are applied to the system before the fault occurrence. Besides, to land the quadrotor affected by the deviation of one of its rotors, the ordinary sliding mode controller is replaced by a modified one. This controlling procedure consists of two steps. First, the deviation angles are identified using the Extended Kalman Filter. Then, these angles are used to calculate the appropriate inputs that are the functions of rotational speed of rotors. The combination of this modified control unit and model-based fault detection method has improved the robustness of the system in the presence of the structural fault and has maintained the system’s controlling performance in fault-free situations.

Quadratic-Kalman-Filter-Based Sensor Fault Detection Approach for Unmanned Aerial Vehicles

Sensors are crucial for the control of unmanned aerial vehicles (UAVs). However, sensor faults will inevitably appear over time. Therefore, it is important to develop a sensor fault detection approach for the reliability of UAV. This article presents a novel model-based UAV fault detection approach based on quadratic Kalman filter (QKF). First, an accurate kinematic and dynamic model of UAVs is established, where the model is linearized and discretized for Kalman filter (KF). Second, the first KF is used for denoising, the secondKF is used to detrend, and residuals are calculated for detection. It is worth mentioning that the second KF is a modified Sage–Husa adaptive KF, which can automatically estimate the measurement noise variance. Compared with traditional approaches, this approach has the advantages of noise reduction, self-adaptation, divergence avoidance, and high detection rate. Simulation and experimental results show the effectiveness of the proposed approach, which can accurately detect the abrupt and incipient fault of an inertial measurement unit (IMU) sensor. At the same time, it can get the higher fault detection rates (FDRs) compared with conventional KF. Furthermore, this approach also provides the leading information and foundation for UAV fault-tolerant control.

Rapid fault reconstruction using a bank of sliding mode observers

This paper deals with the design of a model-based rapid fault detection and isolation strategy using sliding mode observers. To address this problem, a new scheme is proposed by adaptively combining the information provided by a bank of observers. In this regard, a new structure for sliding mode observers is considered. Then, the well-known recursive least square algorithm is utilized to merge individual state estimations suitably such that the system fault is detected faster. The required condition for enhancing perfect state estimation is derived, and the stability of the overall system is proven via Lyapunov’s direct method. The supremacy of proposed scheme is fully discussed through mathematical analyses as well as simulations.

A short-term building energy consumption prediction and diagnosis using deep learning algorithms

Short-term energy consumption prediction of buildings is crucial for developing model-based predictive control, fault detection, and diagnosis methods. This study takes a university library in Xi’an as the research object. First, a time-by-time energy consumption prediction model is established under the supervised learning approach, which uses a long short-term memory (LSTM) network and a Multi-Input Multi-Output (MIMO) strategy. The experimental results validate the model’s validity, which is close enough to physical reality for engineering purposes. Second, the potential of the people flows factor in energy consumption prediction models is explored. The results show that people flow has great potential in predicting building energy consumption and can effectively improve the prediction model performance. Third, a diagnostic method, which can recognize abnormal energy consumption data is used to diagnose the unreasonable use of the building during each hour of operation. The method is based on differences between actual and predicted energy consumption data derived from a short-term energy consumption prediction model. Based on actual building operation data, this work is enlightening and can serve as a reference for building energy efficiency management and operation.

Sensor fault detection and isolation via networked estimation: rank-deficient dynamical systems

This paper considers model-based fault detection of large-scale (possibly rank-deficient) dynamic systems. Assuming only global (and not local) observability over a sensor network, we introduce a single time-scale networked estimator/observer. Sensors take local outputs/measurements of system states with partial observability and share their information (including estimation and/or output) over a communication network, and gain distributed observability. We define the conditions on the network structure ensuring distributed observability and stabilising the error dynamics. However, system outputs are prone to faults and uncertainties, which affect the state estimation of all sensors as a consequence of communicating (possibly) faulty data. From the cyber-physical-systems (CPS) perspective, such faults add bias to the data transferred from the physical layer (dynamic system) to the cyber layer (sensor network). In this work, we propose a localised fault detection and isolation (FDI) mechanism at sensors to secure distributed estimation. This protocol enables every sensor to locally identify the possible fault at the sensor measurement, and, via local detection and isolation, to prevent the spread of biased/faulty information over the network. This distributed isolation and localisation of fault follows from our partial observability assumption instead of full observability at every sensor. Then, other sensors can estimate/track the system by using observationally-equivalent output information to recover for possible loss of observability. In particular, we study rank-deficient systems as they are known to demand more information-sharing, and thus, are more vulnerable to the spread of possible faults over the network. One challenge is the detection of faults in the presence of system/output noise without making (simplifying and unrealistic) upper-bound assumptions on the noise support. We resolve this by adopting probabilistic threshold designs on the residuals. Further, we show that additive faults at rank-deficiency-related outputs affect the residuals at all sensors, a consequence that mandates more constraints on the (distributed) FDI strategy. We address this problem by constrained LMI design of the feedback gain matrix. Finally, we design q-redundant distributed estimators, resilient to isolation/removal of up to q number of faulty sensors, and further, we consider thresholding residual history over a sliding time-window, known as the stateful FDI.

A Vibration Based Automatic Fault Detection Scheme for Drilling Process Using Type-2 Fuzzy Logic

The fault detection system using automated concepts is a crucial aspect of the industrial process. The automated system can contribute efficiently in minimizing equipment downtime therefore improving the production process cost. This paper highlights a novel model based fault detection (FD) approach combined with an interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy system for fault detection in the drilling process. The system uncertainty is considered prevailing during the process, and type-2 fuzzy methodology is utilized to deal with these uncertainties in an effective way. Two theorems are developed; Theorem 1, which proves the stability of the fuzzy modeling, and Theorem 2, which establishes the fault detector algorithm stability. A Lyapunov stabilty analysis is implemented for validating the stability criterion for Theorem 1 and Theorem 2. In order to validate the effective implementation of the complex theoretical approach, a numerical analysis is carried out at the end. The proposed methodology can be implemented in real time to detect faults in the drilling tool maintaining the stability of the proposed fault detection estimator. This is critical for increasing the productivity and quality of the machining process, and it also helps improve the surface finish of the work piece satisfying the customer needs and expectations.

Model-based Fault Detection and Identification of a Quadrotor with Rotor Fault

Model-based Fault Detection and Identification of a Quadrotor with Rotor Fault

Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. Here, a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.

Model-based detection and isolation of the wheel slippage and actuator faults of a holonomic mobile robot

PurposeMobile robots may perform very critical tasks under difficult operating conditions. Faults encountered during their tasks may cause the task to be interrupted or failed completely. In the active fault tolerant control methods, it is very important not only to detect the faults that occur in the robot, but also to isolate these faults to develop a fault recovery strategy that is suitable for that specific type of fault. This study aims to develop a model-based fault detection and isolation method for wheel slippage and motor performance degradation that may occur in wheeled mobile robots.Design/methodology/approachIn the proposed method, wheel speeds can be estimated via the dynamic model of the mobile robot, which includes a friction model between the wheel and the ground. Four residual signals are obtained from the differences between the estimated states and the measured states of the mobile robot. Mobile robot’s faults are detected by using these signals. Also, two different residual signals are generated from the calculation of the traction forces with two different procedures. These six residual signals are then used to isolate possible wheel slippage and performance degradation in a motor.FindingsThe proposed method for diagnosing wheel slip and performance degradation in motors are tested by moving the robot in various directions. According to the data obtained from the test results, a logic table is created to isolate these two faults from each other. Thanks to the created logic table, slippage in any wheel and performance degradation in any motor can be detected and isolated.Originality/valueTwo different recovery strategies are needed to recover temporary wheel slippage and permanent motor faults. Therefore, it is important to isolate these two faults that create similar symptoms in robot’s general movement. Thanks to the method proposed in this study, it is not only possible to isolate the slipping wheel with respect to the non-slipping wheels or to isolate the faulty motor from the non-faulty ones, but also to isolate these two different fault types from each other.

Model-based sensor fault detection, isolation and tolerant control for a mine hoist

Encoder is essential for speed control and very valuable in condition monitoring for a mine hoist and the failure in sensor measurements can lead to serious accidents. In this paper, a novel encoder fault detection, isolation and tolerant control strategy based on finite time observers and constrained fault-tolerant controller is proposed for a mine hoist. The hybrid nonlinear observers which can converge to the origin in finite time are employed to detect and isolate faulty sensors, a residuals evaluation unit is then used to provide reconstructed signals. The constrained fault tolerant controller is presented to guarantee steady and safe running of the mine hoist when sensor fails. This approach is feasible and practical because it does not require a complicated update process, and the fault tolerance controller also makes the hoist run more reliably. Compared with traditional ways, the proposed method has superior performance and can be more effective, which are verified by experimental results.

Gas turbine system identification using a bilayer equilibrium manifold expansion model

PurposeThis paper aims to establish a multi-input equilibrium manifold expansion (EME) model for gas turbine (GT). It proposes that the extension of model input dimension is realized based on similarity theory and affine structure in the framework of single-input EME model. The study aims to expand the scope of application of the EME model so that it can be used for the control or fault diagnosis of GTs.Design/methodology/approachIn this paper, the concepts of corrected equilibrium manifold expansion (CEME) model and multi-cell equilibrium manifold expansion (MEME) model are first proposed. This paper uses theoretical analysis and simulation experiments to demonstrate the effectiveness of the bilayer equilibrium manifold expansion (BEME) model, which is a combination of the CEME and the MEME models. Simulation experiments include confirmatory experiments and comparative experiments.FindingsThe paper provides a new sight into building a multiple-input EME (MI-EME) model for GTs. The proposed method can build an accurate and robust MI-EME model that has superior performance compared with widely used nonlinear models including Wiener model (WM), Hammerstein model (HM), Hammerstein–Wiener model (HWM) and nonlinear autoregressive with exogenous inputs (NARX) network model. In terms of accuracy, the maximum error percentage of the proposed model is just 1.309%, far less than WM, HM and HWM. In terms of the stability of model calculation, the range of the mean error percentage of the proposed model is just a quarter of that of NARX network model.Originality/valueThe paper fulfills the construction of a novel multi-input nonlinear model, which has laid a foundation for the follow-up research of model-based GT fault detection and isolation or GT control.

Model-based Fault Detection of Rotor Position Sensor Fault in Permanent Magnet Synchronous Motor for Electric Vehicle

Abstract Permanent magnet synchronous motors (PMSMs) are widely used in drive system of electric vehicles (EVs) because of their high efficiency, wild torque range, high power density, etc. However, rotor position, as a very critical parameter for its control strategy, often faces inaccuracies in operation. In this paper, the problem of position sensor fault detection in drive system of EV was studied. The mathematical model of EV with a specific drive system was introduced in discrete form and built in Matlab/Simulink. Based on the field oriented control strategy model, a novel fault detection scheme based on the parity space for the drive system was proposed. The proposed method considered the influence of the closed-loop control to the fault detection result. A residual generator and a residual evaluator were included. The simulation results demonstrated the effectiveness and applicability of the proposed method.