Fault Detection Design Research Articles

Research on the transient dynamic characteristics of the low-density polyethylene compressors shaft system with operating pressure exceeding 180 MPa

The safe and stable function of hyper-compressors with operating pressures exceeding 180 MPa is a guarantee for the low-density polyethylene production process, and the transient dynamics of the compressor shaft system under stable operating conditions are an important concern for the design, maintenance, and fault detection of such compressors. Therefore, this paper presents a finite-element model of the hyper-compressor shaft system and analyzes the dynamic stress and fatigue life of the crankshaft, the connecting rod, the crosshead, and the plunger. In order to verify the accuracy of the model, all stages of the compressor's plunger stress and the torque of the crankshaft–motor connection end were tested in the field. The dynamic gas pressure of each stage cylinder was obtained by the tested stress of the corresponding plunger, then it was set as the load input of the finite-element model. The results show that: the average errors of the simulated plunger stress at two stages are 0.14% and 0.21%, respectively; the mean, peak, valley, and range errors of the torque at the crankshaft–motor connection end are 3.80%, 12.37%, and 3.49%, respectively; the simulated first-order torsional natural frequency of the shaft system is 78 Hz with an error of 8.3%. In the occurrence of the first-order torsional resonance, the maximum torsional stress appears at the crankshaft–motor connection end. The maximum dynamic stress alternating amplitude of the hyper-compressor shaft system under stable operating conditions is 103.7 MPa with a minimum life of 3.309 × 109, which occurs at the crankshaft–motor connection end and is converted into 31.48 years. The finite-element model, test, and simulation data presented in this paper can provide a reference for the fault detection and optimization design of hyper-compressors.

Fault Detection and Performance Recovery Design With Deferred Actuator Replacement via a Low-Computation Method

Fault Detection and Performance Recovery Design With Deferred Actuator Replacement via a Low-Computation Method

Application of time‐space neighborhood standardization technology to complex multi‐stage process fault detection

AbstractTo solve the problem that the multi‐stage process with dynamicity and nonlinear is hard to monitor effectively, the time‐space neighborhood standardization (TSNS) method is proposed, which is further applied to partial least squares (PLS) to propose TSNS and PLS (TSNS‐PLS) method for process fault detection. TSNS can transform multi‐stage data into single‐stage data that approximately obeys a standard normal distribution, remove temporal correlation between samples at previous and subsequent moments in the process data, and separate online fault samples. TSNS makes the transformed process data satisfy the requirements of the PLS method for process data and can significantly improve the fault detection rate of the PLS method. Finally, the performance of TSNS‐PLS was examined by a numerical simulation process and the penicillin fermentation process design fault detection experiment.

A Novel, Finite-Time, Active Fault-Tolerant Control Framework for Autonomous Surface Vehicle with Guaranteed Performance

In this paper, a finite-time, active fault-tolerant control (AFTC) scheme is proposed for a class of autonomous surface vehicles (ASVs) with component faults. The designed AFTC framework is based on an integrated design of fault detection (FD), fault estimation (FE), and controller reconfiguration. First, a nominal controller based on the Barrier Lyapunov function is presented, which guarantees that the tracking error converges to the predefined performance constraints within a settling time. Then, a performance-based monitoring function with low complexity is designed to supervise the tracking behaviors and detect the fault. Different from existing results where the fault is bounded by a known scalar, the FE in this study is implemented by a finite-time estimator without requiring any prioir information of fault. Furthermore, under the proposed finite-time AFTC scheme, both the transient and steady-state performance of the ASV can be guaranteed regardless of the occurrence of faults. Finally, a simulation example on CyberShip II is given to confirm the effectiveness of the proposed AFTC method.

Design of Power Transformer Fault Detection of SCADA Alarm Using Fault Tree Analysis, Smooth Holtz–Winters, and L-BFGS for Smart Utility Control Centers

Design of Power Transformer Fault Detection of SCADA Alarm Using Fault Tree Analysis, Smooth Holtz–Winters, and L-BFGS for Smart Utility Control Centers

An Overview of Grounding Design and Grounding Fault Detection and Location Methods for a Multiphase Rectifier Generator Power Supply System

A multiphase rectifier generator is important power generation equipment in DC power systems in transportation fields such as ships and aviation. Grounding design and grounding fault detection and positioning are key technologies for the safe operation of the power system. This article aims to systematically elaborate on the current research status of its related content. Firstly, the topological structure characteristics of the multiphase rectifier generator power supply system are introduced, the advantages and disadvantages of different grounding methods, position selection, and other design schemes are analyzed, and reasonable suggestions are given for the selection of grounding resistance. Secondly, a brief introduction is given to the research progress on ground fault detection and location in the medium voltage DC integrated power systems of ships, and the characteristics of typical methods for ground fault detection and location in DC power grid systems are summarized. Finally, the future research directions are outlined.

From Generalized Gauss Bounds to Distributionally Robust Fault Detection With Unimodality Information

The need for exact distributions in probabilistic fault detection design is hardly fulfilled. The recent moment-based distributionally robust fault detection (DRFD) design secures robustness against inexact distributions but suffers from over-pessimism. To address this issue, we develop a new DRFD design scheme by using unimodality, a ubiquitous property of real-life distributions. To evaluate worst-case false alarm rates, a new generalized Gauss bound is first attained, which is less conservative than known Chebyshev bounds that underpin moment-based DRFD. This also yields analytical solutions to DRFD design problems, which are suboptimal but provably less conservative than known ones disregarding unimodality. A tightened Gauss bound is further derived by assuming bounded uncertainty, based on which convex programming approximation of DRFD problems is developed. Results on physical system data elucidate that the proposed DRFD design can reduce conservatism of moment-based ones by using unimodality information, and attaining a better robustness-sensitivity trade-off than prevalent data-centric design with moderate sample sizes.

A Hybrid Design of Fault Detection for Nonlinear Systems Based on Dynamic Optimization.

To ensure the safety of an automation system, fault detection (FD) has become an active research topic. With the development of artificial intelligence, model-free FD strategies have been widely investigated over the past 20 years. In this work, a hybrid FD design approach that combines data-driven and model-based is developed for nonlinear dynamic systems whose information is not known beforehand. With the aid of a Takagi-Sugeno (T-S) fuzzy model, the nonlinear system can be identified through a group of least-squares-based optimization. The associated modeling errors are taken into account when designing residual generators. In addition, statistical learning is adopted to obtain an upper bound of modeling errors, based on which an optimization problem is formulated to determine a reliable FD threshold. In the online FD decision, an event-triggered strategy is also involved in saving computational costs and network resources. The effectiveness and feasibility of the proposed hybrid FD method are illustrated through two simulation studies on nonlinear systems.

Fault Detection and Reliable Controller Design for Fractional-Order Systems Based on Dynamic Observer

In this paper, the problem of the fractional-order linear systems’ simultaneous fault detection and reliable control (SFDC) in the finite frequency domain is investigated. A dynamic observer aimed at detecting faults is designed, which also generates state estimation signals. In particular, it is found that the proposed dynamic-observer-based controller can achieve a better H∞ performance. We derived the detector and controller’s design conditions to achieve H− fault sensitivity performance and H∞ interference attenuation performance in the limited frequency domain based on the generalized KYP lemma, which can achieve a better disturbance attenuation performance and fault sensitivity performance. Finally, the simulation results show the designed method’s effectiveness.

A Robust Technique for Detection, Diagnosis, and Localization of Switching Faults in Electric Drives Using Discrete Wavelet Transform

Detection, diagnosis, and localization of switching faults in electric drives are extremely important for operating a large number of induction motors in parallel. This study aims to present the design and development of switching fault detection, diagnosis, and localization strategy for the induction motor drive system (IMDS) by using a novel diagnostic variable that is derived from discrete wavelet transform (DWT) coefficients. The distinctiveness of the proposed algorithm is that it can identify single/multiple switch open and short faults and locate the defective switches using a single mathematical computation. The proposed algorithm is tested by simulation in MATLAB/Simulink and experimentally validated using the LabVIEW hardware-in-the-loop platform. The results demonstrate the robustness and effectiveness of the proposed technique in identifying and locating faults.

Sensor FDI System Based on Discrete‐TimeFractional‐Order Observers

The design of sensor fault detection and isolation (FDI) systems has been studied widely by several authors of different fields of science (engineering, mathematics, physics) to ensure the continuous operation of systems or processes. This research presents the design and evaluation of a fault detection and isolation (FDI) system based on discrete fractional‐orderhigh‐gain observers (DFOHGO) for the outlet temperature sensors of a heat exchanger. The formulation of the discrete fractional‐order observers is based on the Grünwald–Letnikov fractional‐order derivative. The FDI system is based on a bank of two DFOHGOs to carry out a residual evaluation. The obtained results showed that the DFOHGOs provide accurate estimations of the temperatures. This allows switching the faulty sensor signal to the temperature estimations provided by the DFOHGOs when a fault occurs.

Active Current Sensor Fault-Tolerant Control of Induction Motor Drive with Online Neural Network-Based Rotor and Stator Resistance Estimation

Abstract This article presents an active current sensor (CS) fault-tolerant control (FTC) strategy for induction motor (IM) drive with adaptation of rotor and stator resistances. The stator current estimator with online adaptation of resistance parameters was applied for the reconstruction of missing current signals. A model reference adaptive system (MRAS), based on a neural network (NN), was used to estimate the rotor resistance. Additionally, stator resistance estimation was applied based on ratio index. The use of such a solution allowed for a significant increase in the quality of stator current reconstruction, which is particularly important for the design of CS fault detection (FD) and compensation algorithms. A wide range of simulation studies have been carried out for different operating conditions of the IM drive. The results showed that applying rotor and stator resistance estimation can improve the quality of stator current estimation by up to approximately 95% under rated operating point. The study was carried out for nominal and low speeds, with two, one, and without healthy CS.

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.

Simultaneous fault detection and control design for DC–AC converter with a neutral leg based on dynamic observer

In this paper, the problem of simultaneous fault detection and control (SFDC) for a 3-phase 4-wire converter with neutral bridge arm, which is commonly used in the application of smart grid and distributed generation, is considered. By employing a detector/controller module consisting of a dynamic observer and a state feedback controller based on the dynamic observer, a multi-objective H∞ optimization SFDC framework is established to achieve the fault detection and control objectives simultaneously. Extended linear matrix inequalities (LMIs) characterizations are obtained as the strict conditions for solvability of the SFDC problem. The proposed method can reduce the designing conservativeness and overcome the shortcoming of existing SFDC methods by applying the superiority of dynamic observer. The achievable performance is analyzed quantitatively and the effectiveness of our designing method was illustrated and verified both in simulation and the hardware-in-the-loop(HIL) simulation which is conducted in a real-time simulation platform built by StarSim and MT respectively.

Analysis of Vibrations and Currents for Broken Rotor Bar Detection in Three-phase Induction Motors

Selecting the physical property capable of representing the health state of a machine is an important step in designing fault detection systems. In addition, variation of the loading condition is a challenge in deploying an industrial predictive maintenance solution. The robustness of the physical properties to variations in loading conditions is, therefore, an important consideration. In this paper, we focus specifically on squirrel cage induction motors and analyze the capabilities of three-phase current and five vibration signals acquired from different locations of the motor for the detection of Broken Rotor Bar generated in different loads. In particular, we examine the mentioned signals in relation to the performance of classifiers trained with them. Regarding the classifiers, we employ deep conventional classifiers and also propose a hybrid classifier that utilizes contrastive loss in order to mitigate the effect of different variations. The analysis shows that vibration signals are more robust under varying load conditions. Furthermore, the proposed hybrid classifier outperforms conventional classifiers and is able to achieve an accuracy of 90.96% when using current signals and 97.69% when using vibration signals.

Development of a Fault Detection Instrument for Fiber Bragg Grating Sensing System on Airplane.

This study develops a fault detection device for the fiber Bragg grating (FBG) sensing system and a fault detection method to realize the rapid detection of the FBG sensing system on airplanes. According to the distribution of FBG sensors on airplanes, the FBG sensing system is built based on wavelength division multiplexing (WDM) and space division multiplexing (SDM) technologies. Furthermore, the hardware and software of the fault detection device and the relevant FBG demodulator are studied in detail. Additionally, in view of the similar features of the healthy FBG sensor in the same measuring point, a rapid fault diagnosis method based on a synthetical anomaly index is proposed. The features (light intensity I, signal length L, standard deviation of original sample and energy value in time-domain P) of FBG sensors are extracted. The aggregation center value of the above feature values is obtained through the loop iteration method. Furthermore, the separation degrees of features are calculated and then form the synthetical anomaly index so as to make an effective diagnosis of the state of the FBG sensor. Finally, the designed fault detection instrument and proposed fault detection method are used to monitor the 25 FBG sensors on the airplane, the results indicated that three faulty and two abnormal FBG sensors on the airplane are identified, showing the effectiveness of the proposed fault detection method.

Robust adaptive fault detection and diagnosis observer design for a class of nonlinear systems with uncertainty and unknown time-varying internal delay

This paper introduces a novel robust adaptive fault detection and diagnosis (FDD) observer design approach for a class of nonlinear systems with parametric uncertainty, unknown system fault and time-varying internal delays. The conditions for the existence of the proposed FDD are obtained based on the well-known Linear Matrix Inequalities (LMI) technique. Using Lyapunov stability theory, the adaptation laws for updating the observer weights and unknown faults estimation are derived based on which the convergence of the state estimation error to zero and asymptotic stability of the error dynamics are proven. Toward this, a new structural algorithm for FDD observer design is also derived based on LMIs. The performance of the proposed method is also investigated while applying to some industrial systems. Simulation results illustrate superior performance of the proposed method for the systems subject to time-varying unknown delays on states, uncertainty in nonlinear system modeling and unknown system faults.

Hybrid-triggered H∞ Fault Detection for Distributed Time-delay Systems with Communication Quantization Based on T-S Fuzzy Model

In the network environment, the time-triggered mechanism wastes limited bandwidth resources due to the transmission of all sampled data to the network. The event-triggered mechanism may increase system errors due to ignoring factors such as changes in network utilization. In order to reduce the design conservatism, this paper studies the design of a hybrid-triggered H∞ fault detection filter for a class of nonlinear networked control systems described by Takagi-Sugeno (T-S) fuzzy model, and applies quantization techniques in the communication channel. Using the Lyapunov-Krasovskii functional and integral inequality methods, new results on the stability and H∞ performance of fuzzy fault detection systems are presented. In particular, the designed fault detection filter has a specific H∞ noise attenuation level γ. The final simulation results verify the effectiveness of the design.

Code phase tracking error based autonomous integrity monitoring for GNSS/INS ultra-tightly integrated system

The ultra-tight integration of GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System) may suffer from faults in GNSS, INS and integration filter. Both the faults occurred at the current epoch and at previous epochs affect the position solution deviation of ultra-tight integration at the current epoch, and the effect is reflected in GNSS code phase tracking errors. In this paper, a new autonomous integrity monitoring algorithm is proposed for the federated GNSS/INS ultra-tight integrations in which the code phases of local GNSS signal replicas for GNSS signal tracking are completely derived from the integrated navigation solution. In the proposed algorithm, GNSS code phase tracking errors are employed to construct the test statistics for fault detection through a MHSS (Multiple Hypothesis Solution Separation) method improved from the classical MHSS used in ARAIM (Advanced Receiver Autonomous Integrity Monitoring). With the improved MHSS, the proposed algorithm can account for GNSS nominal biases, multiple GNSS faults and the faults in INS and integration filter. The protection levels under multiple hypothesis are also derived. A semi-physical simulation of the federated ultra-tight integration of GPS (Global Positioning System) and INS in airplane approaching case and a vehicle-based field test of the federated ultra-tight integration of GPS, Galileo and INS are conducted to validate the proposed algorithm. The experiments show that the designed fault detection can effectively detect the occurred faults and the derived protection levels can correctly bound the caused position solution error.

Surface Discharges Performance of ETFE- and PTFE-Insulated Wires for Aircraft Applications

Compared to their predecessors, the next generations of aircrafts will be more electrified, require more electrical power and operate at higher voltage levels to meet strict weight and volume constraints. The combined effect of low-pressure environments, increased voltage levels and compact designs intensifies the risks of premature insulation degradation due to electrical discharge activity. This paper studies the resistance to surface discharges of PTFE (polytetrafluoroethylene) and ETFE (ethylene tetrafluoroethylene), two insulation materials widely used in today’s aircraft wiring systems due to their outstanding properties, such as a wide temperature operation range and a high dielectric strength. The study is carried out in a low-pressure chamber, which was pressurized within the pressure range of 10–100 kPa that includes most aircraft applications. There is a compelling need for experimental data to assess the resistance of insulation materials to surface discharges at a very early stage as a function of the environmental pressure. Data on resistance to surface discharges in low-pressure environments for aeronautical applications are lacking, while most standards for insulation systems are based on tests under standard pressure conditions. The results provided in this work can be useful to design wiring systems for future more electric aircrafts, as well as to design fault detection systems for an early detection and identification of faults related to surface discharges. Therefore, the data and analysis included in this paper could be of great interest to design and develop insulation systems for wiring systems and standard assessment methods, as well as to design fault detection strategies for the early detection and identification of surface discharges for future generations of more electric aircrafts.