Proposed Fault Detection Method Research Articles

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions.

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries.

This paper presents a new fault detection system in hypnotic sensors used for general anesthesia during surgery. Drug infusion during surgery is based on information received from patient monitoring devices; accordingly, faults in sensor devices can put patient safety at risk. Our research offers a solution to cope with these undesirable scenarios. We focus on the anesthesia process using intravenous propofol as the hypnotic drug and employing a Bispectral Index (BISTM) monitor to estimate the patient’s unconsciousness level. The method developed identifies BIS episodes affected by disturbances during surgery with null clinical value. Thus, the clinician—or the automatic controller—will not take those measures into account to calculate the drug dose. Our method compares the measured BIS signal with expected behavior predicted by the propofol dose provider and the electromyogram (EMG) signal. For the prediction of the BIS signal, a model based on a hybrid intelligent system architecture has been created. The model uses clustering combined with regression techniques. To validate its accuracy, a dataset taken during surgeries with general anesthesia was used. The proposed fault detection method for BIS sensor measures has also been verified using data from real cases. The obtained results prove the method’s effectiveness.

Weak thruster fault detection for AUV based on stochastic resonance and wavelet reconstruction

When the bi-stable stochastic resonance method was applied to enhance weak thruster fault for autonomous underwater vehicle (AUV), the enhancement performance could not satisfy the detection requirement of weak thruster fault. As for this problem, a fault feature enhancement method based on mono-stable stochastic resonance was proposed. In the method, in order to improve the enhancement performance of weak thruster fault feature, the conventional bi-stable potential function was changed to mono-stable potential function which was more suitable for aperiodic signals. Furthermore, when particle swarm optimization was adopted to adjust the parameters of mono-stable stochastic resonance system, the global convergent time would be long. An improved particle swarm optimization method was developed by changing the linear inertial weighted function as nonlinear function with cosine function, so as to reduce the global convergent time. In addition, when the conventional wavelet reconstruction method was adopted to detect the weak thruster fault, undetected fault or false alarm may occur. In order to successfully detect the weak thruster fault, a weak thruster detection method was proposed based on the integration of stochastic resonance and wavelet reconstruction. In the method, the optimal reconstruction scale was determined by comparing wavelet entropies corresponding to each decomposition scale. Finally, pool-experiments were performed on AUV with thruster fault. The effectiveness of the proposed mono-stable stochastic resonance method in enhancing fault feature and reducing the global convergent time was demonstrated in comparison with particle swarm optimization based bi-stochastic resonance method. Furthermore, the effectiveness of the proposed fault detection method was illustrated in comparison with the conventional wavelet reconstruction.

Open Fault Detection and Tolerant Control for a Five Phase Inverter Driving System

This paper proposes a fault detection and the improved fault-tolerant control for an open fault in the five-phase inverter driving system. The five-phase induction machine has a merit of fault-tolerant control due to its increased number of phases. This paper analyzes an open fault pattern of one switch and proposes an effective fault detection method based upon this analysis. The proposed fault detection method using the analyzed patterns is applied in the power inverter. In addition, when the open fault occurs in the one switch of the induction machine driving system, the proposed fault-tolerant control method is used to operate the induction machine using the remaining healthy phases, after performing the fault detection method. Simulation and experiment results are provided to validate the proposed technique.

Online Turn Fault Detection of Interior Permanent-Magnet Machines Using the Pulsating-Type Voltage Injection

This paper proposes a stator winding fault detection for interior permanent-magnet (PM) machines based on the pulsating-type voltage injection. By superimposing a pulsating voltage on d-axis with respect to the rotor-referred synchronous frame, measurable winding fault reflected signals occur in the injection induced current ripples. Comparing to prior diagnostic techniques, the proposed injection method is immune to the rotor saliency because saliency reflected current harmonic is decoupled once the pulsating voltage is exactly injected on rotor d-axis. It results in the improved fault detection performance on interior PM (IPM) machines as well as other salient machines. An IPM machine with three sets of turn-to-turn short is built to verify the proposed fault detection method.

Neural Approach to Fault Detection in Three-phase Induction Motors

Three-phase induction motors play a key role in electromotive force production, although their widespread use in industrial applications leads to a variety of defects that can impair their normal operation. This paper proposes an alternative method, based on artificial neural networks, for classifying and detecting bearing faults in three-phase induction motors connected directly to the power grid. Experimental tests conducted in the laboratory consider practical conditions such as voltage unbalances and differing load torques. Analyses are performed in the time domain, and are based on electric motor quantities such as voltages and currents, which are acquired considering a half-cycle of the voltage grid. The performance and efficacy of the proposed fault detection method is evaluated and validated by using a personal computer to conduct online experimental tests, and by embedding in digital signal processors. The proposed method is also tested in electrical machines used in the sugar cane industry.

Fault detection filter design for networked multi‐rate systems with fading measurements and randomly occurring faults

In this study, the fault detection (FD) problem is investigated for a class of networked multi-rate systems (NMSs) with network-induced fading channels and randomly occurring faults. The stochastic characteristics of the fading measurements are governed by mutually independent random channel coefficients over the known interval [0,1]. By applying the lifting technique, the system model for the observer-based FD is established. With the aid of the stochastic analysis approach, sufficient conditions are established under which the stochastic stability of the error dynamics for the state estimation is guaranteed and the prescribed H ∞ performance constraint on the error dynamics for the fault estimation is achieved. Based on the established conditions, the addressed FD problem of NMSs is recast as a convex optimisation one that can be solved via the semi-definite program method, and the explicit expression of the desired FD filter is derived by means of the feasibility of certain matrix inequalities. The main results are specialised to the networked single-rate systems that are a special case of the NMSs. Finally, two simulation examples are utilised to illustrate the effectiveness of the proposed FD method.

A fault detection scheme based on minimum identified uncertainty bounds violation for broken rotor bars in induction motors

In this paper, a novel method for broken bars fault detection in the case of three-phase induction motors and under different payloads will be presented and experimentally evaluated. In the presented approach, the cases of a partially or full broken rotor bars are being also considered, caused by: (a) drilling 4mm and 8mm out of the 17mm thickness of the same rotor bar and (b) fully drilled (17mm) one, two and three broken bars. The proposed fault detection method is based on the Set Membership Identification (SMI) technique and a novel proposed minimum boundary violation fault detection scheme, applied on the identified motor's parameters. The system identification procedure is being carried out on the simplified equivalent model of the induction motor, during the steady-state operation (non-fault case), while at the same time the proposed scheme is able to calculate on-line the corresponding safety bounds for the identified variables, based on a priori knowledge of the measuring corrupting noise (worst case encountered). The efficiency, the robustness and the overall performance of the established fault detection scheme is being extensively evaluated in multiple experimental studies and under various time instances of faults and load conditions.

A Data-driven Fault Detection Method Based on Dissipative Trajectories

Fault detection is becoming increasingly important as the complexity of industrial process develops. In this paper, a data-driven fault detection method is proposed. The dissipativity theory is adopted to find the appropriate dissipativity properties for the process input output trajectory. The dissipativity properties can be viewed as an Abstract energy property, and the dissipativity properties of input output trajectories represent process dynamic features. As faults occur, the dissipative trajectories will change thus allow fault detection to be performed based on these dissipativity properties. A training algorithm is developed to search for the related properties using input output data. A prior knowledge of the process can be incorporated into the algorithm to facilitate the training. The proposed fault detection method is illustrated on a case study of a mono-chlorobenzene plant simulated using VMGSim.

Fault Detection for Two-Dimensional Roesser Systems With Sensor Faults

This paper investigates the problem of fault detection for 2-D systems with lock-in-place sensor faults. Our attention is focused on detecting sensor faults in the presence of disturbances. To this end, a fault detection filter is designed, through which a residual signal is generated for both the fault-free and faulty cases. In light of the generalized Kalman–Yakubovich–Popov lemma for 2-D systems and matrix inequality techniques, convex filter design conditions are derived. Based on these conditions, an algorithm is proposed to calculate the parameters of a desired fault detection filter. Moreover, a residual evaluation function and a threshold are proposed for 2-D systems. Finally, an example is employed to illustrate the effectiveness of the proposed fault detection method.

Distributed Scan Like Fault Detection and Test Optimization for Digital Microfluidic Biochips

Development of digital microfluidic biochips (DMFB) has faced a major setback from the threats of faulty and erroneous fluidic operations. Defective electrodes are the main reason for this misleading assay performance. It also affects the assay completion time and overall turnaround time. In this present article, a fast fault diagnosis mechanism is discussed to identify the defective locations of electrode array. The proposed fault detection method is governed by a distributed dispensing and scheduling of test droplets on a 2-D biochip. Water droplets are strategically routed across the DMFB board and quantified at every location using cost effective photodiode sensors. Multiple test droplets are routed on the chip in a time synchronized manner to avoid any routing conflict or failure in diagnosis. This concurrent test droplet circulation incurs optimum layover period and parallel and multiple test droplet movement enhances the fault detection performance. Completeness of the fault analysis is ensured with a sequential post processing as well. Test results of this approach have recorded some substantial improvement in terms of fault detection time and accuracy.

Recurrence Plot Based Damage Detection Method by Integrating Control Chart

Because of the importance of damage detection in manufacturing systems and other areas, many fault detection methods have been developed that are based on a vibration signal. Little work, however, has been reported in the literature on using a recurrence plot method to analyze the vibration signal for damage detection. In this paper, we develop a recurrence plot based fault detection method by integrating the statistical process control technique. The recurrence plots of the vibration signals are derived by using the recurrence plot (RP) method. Five types of features are extracted from the recurrence plots to quantify the vibration signals’ characteristic. Then, the control chart, a multivariate statistical process control technique, is used to monitor these features. The control chart technique, however, has the assumption that all the data should follow a normal distribution. The RP based bootstrap control chart is proposed to estimate the control chart parameters. The performance of the proposed RP based bootstrap control chart is evaluated by a simulation study and compared with other univariate bootstrap control charts based on recurrence plot features. A real case study of rolling element bearing fault detection demonstrates that the proposed fault detection method achieves a very good performance.

An Online Method for Static Eccentricity Fault Detection in Axial Flux Machines

This paper investigates a novel online method for static eccentricity (SE) fault detection of single-stator-single-rotor axial flux permanent-magnet (AFPM) machines. The method allows for both estimating the SE factor and computing the minimum air-gap position. As direct flux measurements are expensive, the proposed method for fault detection is based on the measurement of the voltages of three search coils uniformly distributed in the air gap. Unlike the current spectrum-based fault detection methods on which the winding configuration has a considerable impact, the proposed method can be used for any type of AFPM machine independently on the winding layout. A simplified 2-D analytical approach is used. Moreover, a circuit-coupled 3-D finite-element method (FEM) was used to compute the induced voltages in the search coils and show the validity of the method through 20 eccentric scenarios. Moreover, an experimental setup was built to validate the analytical approach and to confirm the results achieved with the 3-D FEM. Several experimental measurements have been done. All measurements confirm the accuracy of the proposed method. It is also shown that the method yields accurate results under any load condition.

Fault detection in nonlinear systems based on type-2 fuzzy sets and bat optimization algorithm

In this paper, a new method of fault detection is proposed based on interval type-2 fuzzy systems. The main idea is to provide a confident span using interval type-2 fuzzy sets. Bat algorithm, as a metaheuristic method, is used to optimize the parameters of the system. In other words, upper and lower bounds of the interval type-2 fuzzy system are estimated by means of two optimal fuzzy functions. The proposed fault detection method has been tested in a non-linear system, a two-tank with a fluid flow. Simulation results show that the proposed method is very strong and effective.

Open-Circuit Fault Diagnosis and Fault-Tolerant Control for a Grid-Connected NPC Inverter

This paper presents an open-circuit fault detection method for a grid-connected neutral-point clamped (NPC) inverter system. Further, a fault-tolerant control method under an open-circuit fault in clamping diodes is proposed. Under the grid-connected condition, it is impossible to identify the location of a faulty switch by the conventional methods which usually use the distortion of outputs because the distortion of the outputs is the same in some fault cases. The proposed fault detection method identifies the location of the faulty switch and the faulty clamping diode of the NPC inverter without any additional hardware or complex calculations. In the case of the clamping diode faults, the NPC inverter can transfer full rated power with sinusoidal currents by the proposed fault-tolerant control. The feasibility of the proposed fault detection and the fault-tolerant control methods for the grid-connected NPC inverter are verified by simulation and experimental results.

Fault detection in finite frequency domains for multi‐delay uncertain systems with application to ground vehicle

SummaryThis paper is concerned with the actuator fault detection (FD) problem in finite frequency domains for multi‐delay systems subject to time‐varying affine uncertainties. Because of the existence of time‐varying uncertain parameters, the generalized Kalman–Yakubovic–Popov lemma based finite frequency FD filter design approaches cannot be applied. To tackle this difficulty, a new delay‐dependent bounded real lemma (BRL) is established by using Lyapunov theory and Parseval's theorem to characterize the finite frequency disturbance attenuation and fault sensitivity performances. Moreover, via the obtained BRL, convex FD filter design conditions are then derived by constructing a hyperplane tangent. Finally, the effectiveness and advantages of the proposed FD method are illustrated through a simulation example on a ground vehicle. Copyright © 2014 John Wiley & Sons, Ltd.

A Grid Voltage Measurement Method for Wind Power Systems during Grid Fault Conditions

Grid codes in many countries require low-voltage ride-through (LVRT) capability to maintain power system stability and reliability during grid fault conditions. To meet the LVRT requirement, wind power systems must stay connected to the grid and also supply reactive currents to the grid to support the recovery from fault voltages. This paper presents a new fault detection method and inverter control scheme to improve the LVRT capability for full-scale permanent magnet synchronous generator (PMSG) wind power systems. Fast fault detection can help the wind power systems maintain the DC-link voltage in a safe region. The proposed fault detection method is based on on-line adaptive parameter estimation. The performance of the proposed method is verified in comparison to the conventional voltage measurement method defined in the IEC 61400-21 standard.

A fuzzy neural network approach for online fault detection in waste water treatment process

In this paper, an effective strategy for fault detection of sludge volume index (SVI) sensor is proposed and tested on an experimental hardware setup in waste water treatment process (WWTP). The main objective of this fault detection strategy is to design a system which consists of the online sensors, the SVI predicting plant and fault diagnosis method. The SVI predicting plant is designed utilizing a fuzzy neural network (FNN), which is trained by a historical set of data collected during fault-free operation of WWTP. The fault diagnosis method, based on the difference between the measured concentration values and FNN predictions, allows a quick revealing of the faults. Then this proposed fault detection method is applied to a real WWTP and compared with other approaches. Experimental results show that the proposed fault detection strategy can obtain the fault signals of the SVI sensor online.

FAULT DETECTION FOR THE CLASS IMBALANCE PROBLEM IN SEMICONDUCTOR MANUFACTURING PROCESSES

In the semiconductor manufacturing process, fault detection which aims at constructing a decision tool to maintain high process yields is a major step of the process control. Unfortunately, the class imbalance in the modern semiconductor industry makes feature selection for fault detection quite challenging. However, the characteristic has usually been ignored in the open literatures. This paper analyzes the challenge and indicates some of the reasons are due to the dataset shift, the small samples and the class overlapping caused by the class imbalance. To cope with the problems, a new feature selection approach is proposed, which combines the global and local resampling and named ensemble manifold sensitive margin fisher analysis (EMSMFA). Our approach consists of three key components: (1) At the global level, the bagging-based ensemble model is used to overcome the overfitting caused by the data shift; (2) At the local level, the manifold-based oversampling named the weighted synthetic minority oversampling technique (WSMOTE) is proposed to solve the small samples problem in the minority class; (3) The sensitive margin fisher analysis (SMFA) is used to solve the challenge caused by the class overlapping. The proposed fault detection method is demonstrated through its application to the semiconductor wafer fabrication process. The experimental results confirm the EMSMFA improves the performance of fault detection.

Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA

A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA) with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT). Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU).