Proposed Diagnosis Approach Research Articles

Polymer electrolyte membrane fuel cell fault diagnosis based on empirical mode decomposition

Diagnosis tool for water management is relevant to improve the reliability and lifetime of polymer electrolyte membrane fuel cells (PEMFCs). This paper presents a novel signal-based diagnosis approach, based on Empirical Mode Decomposition (EMD), dedicated to PEMFCs. EMD is an empirical, intuitive, direct and adaptive signal processing method, without pre-determined basis functions. The proposed diagnosis approach relies on the decomposition of FC output voltage to detect and isolate flooding and drying faults. The low computational cost of EMD, the reduced number of required measurements, and the high diagnosis accuracy of flooding and drying faults diagnosis make this approach a promising online diagnosis tool for PEMFC degraded modes management.

Transient-Based Rotor Cage Assessment in Induction Motors Operating With Soft Starters

The reliable assessment of the rotor condition in induction motors is a matter of increasing concern in the industry. Although rotor damages are more likely in line-started motors operating under high inertias, some cases of broken rotor bars in motors supplied via soft starters have been also reported in the industry. Motor current signature analysis (MCSA) is the most widely spread approach to diagnose such failures. However, its serious drawbacks in many real industrial applications have encouraged investigation on alternative methods enhancing the reliability of the diagnosis. This paper extends a recently introduced diagnosis methodology relying on the startup current analysis to the case of soft-starter-operated motors. The approach has proven to provide very satisfactory results, even in cases where the classical MCSA does not lead to correct diagnosis conclusions. However, its extension to operation under soft starters was still a pending issue. The experimental results shown in this paper ratify the validity of the proposed diagnosis approach in soft-starter-operated induction motors.

A DFIM Sensor Faults Multi-Model Diagnosis Approach Based on an Adaptive PI Multiobserver—Experimental Validation

This paper studies the problem of diagnosis strategy for a doubly fed induction motor (DFIM) sensor faults. This strategy is based on unknown input proportional integral (PI) multiobserver. Thecontribution of this paper is on one hand the creation of a new DFIM model based on multi-model approach and, on the other hand, the synthesis of an adaptive PI multi-observer. The DFIM Volt per Hertz drive system behaves as a nonlinear complex system. It consists of a DFIM powered through a controlled PWM Voltage Source Inverter (VSI). The need of a sensorless drive requires soft sensors such as estimators or observers. In particular, an adaptive Proportional-Integral multi-observer is synthesized in order to estimate the DFIM’s outputs which are affected by different faults and to generate the different residual signals symptoms of sensor fault occurrence. The convergence of the estimation error is guaranteed by using the Lyapunov’s based theory. The proposed diagnosis approach is experimentally validated on a 1 kW Induction motor. Obtained simulation results confirm that the adaptive PI multiobserver consent to accomplish the detection, isolation and fault identification tasks with high dynamic performances.

Vibration signature analysis for monitoring rotor broken bar in double squirrel cage induction motors based on wavelet analysis

Purpose – The purpose of this paper is to present a diagnosis technique, for rotor broken bar in double cage induction motor, based on advanced use of wavelet transform analysis. The proposed technique is experimentally validated. Design/methodology/approach – The proposed approach is based on a combined use of frequency sliding and wavelet transform analysis, to isolate the contribution of the rotor fault components issued from vibration signals in a single frequency band. Findings – The proposed technique is reliable for tracking the rotor fault components over time-frequency domain. The quantitative analysis results based on this technique are the proof of its robustness. Research limitations/implications – The validity of the proposed diagnosis approach is not limited to the analysis under steady-state operating conditions, but also for time-varying conditions where rotor fault components are spread in a wide frequency range. Practical implications – The developed approach is best suited for automotive or high power traction systems, in which safe-operating and availability are mandatory. Originality/value – The paper presents a diagnosis technique for rotor broken bar in double cage induction motor base on advanced use of wavelet transform which allows the extraction of the most relevant rotor fault component issued from axial vibration signal and clamping it in a single frequency bandwidth, avoiding confusions with other components and false interpretations.

Board-Level Functional Fault Diagnosis Using Multikernel Support Vector Machines and Incremental Learning

Advanced machine learning techniques offer an unprecedented opportunity to increase the accuracy of board-level functional fault diagnosis and reduce product cost through successful repair. Ambiguous or incorrect diagnosis results lead to long debug times and even wrong repair actions, which significantly increase repair cost. We propose a smart diagnosis method based on multikernel support vector machines (MK-SVMs) and incremental learning. The MK-SVM method leverages a linear combination of single kernels to achieve accurate faulty-component classification based on the errors observed. The MK-SVMs thus generated can also be updated based on incremental learning, which allows the diagnosis system to quickly adapt to new error observations and provide even more accurate fault diagnosis. Two complex boards from industry, currently in volume production, are used to validate the proposed diagnosis approach in terms of diagnosis accuracy (success rate) and quantifiable improvements over previously proposed machine-learning methods based on several single-kernel SVMs and artificial neural networks.

Advanced Diagnosis of Electrical Faults in Wound-Rotor Induction Machines

The aim of this paper is to present a diagnosis methodology for the detection of electrical faults in three-phase wound-rotor induction machines (WRIMs). In the considered application, the rotor windings are supplied by a static converter for the control of active and reactive power flows exchanged between the machine and the electrical grid. The proposed diagnosis approach is based on the use of wavelet analysis improved by a preprocessing of the rotor-voltage commands under time-varying conditions. Thus, the time evolution of fault components can be effectively analyzed. This paper proves also the importance of the fault components computed from rotor voltages in comparison to those coming from rotor currents under closed-loop operation. A periodical quantification of the fault, issued from the wavelet analysis, has been introduced for accurate stator- or rotor-fault detection. Simulation and experimental results show the validity of the proposed method, leading to an effective diagnosis procedure for both stator and rotor electrical faults in WRIMs.

Fusing of Multi-Channel Sensors for Power Station Fault Diagnosis in Marine Power Systems

Fault diagnosis of the marine power station is essential to ensure the normal electric supply for the whole ship. In this paper, a new faults diagnosis technique for the power station using the data fusion technique has been proposed. The vibration signals of the power station were recorded by the multi-channel sensors. The independent component analysis (ICA) was adopted as the data fusion approach to find the characteristic vibration signals of the power station faults. Then the wavelet packet was employed to extract the feature vector of the fused vibration signals. In addition, the oil particle features has been extracted using the oil analysis. Lastly, the least square support vector machines (LS-SVM) was used to recognize the fault patterns of the power station. Moreover, the improved particle swarm optimization (PSO) was employed to enhance the learning ability of the LS-SVM. The experimental tests were implemented in a real ship to evaluate the effectiveness of the proposed diagnosis approach. The diagnosis results have shown that distinguished fault features have been extracted and the fault identification accuracy is acceptable. In addition, the classification rate of the proposed method is superior to the traditional SVM based method. DOI: http://dx.doi.org/10.5755/j01.eee.19.5.2224

Diagnosis of artificially created surface damage levels of planet gear teeth using ordinal ranking

Effective diagnosis of damage levels is important for condition based preventive maintenance of gearboxes. One special characteristic of damage levels is the inherent ordinal information among different levels. Retaining the ordinal information is therefore important for diagnosing damage levels. Classification, a machine learning technique, has been widely adopted for automated diagnosis of gear faults. However, classification cannot keep the ordinal information because the damage levels are treated as nominal variables. This paper employs ordinal ranking, another machine learning technique, to preserve the ordinal information in automated diagnosis of damage levels. As to ordinal ranking, feature selection is important. However, most existing feature selection methods are for classification, which are not suitable for ordinal ranking. This paper designs a feature selection method for ordinal ranking based on correlation coefficients. A diagnosis approach based on ordinal ranking and the proposed feature selection method is then introduced. This method is tested on diagnosis of artificially created surface damage levels of planet gear teeth in a planetary gearbox. Experimental results show the effectiveness of the proposed diagnosis approach. The advantages of using ordinal ranking for diagnosing gear damage levels are also demonstrated.

A New Intelligent Fusion Method of Multi-Dimensional Sensors and Its Application to Tribo-System Fault Diagnosis of Marine Diesel Engines

Marine diesel engines, a critical component to provide power for entire ships, have been received and still need considerable attentions to ensure their safety operation. Vibration and wear debris analysis are currently the most popular techniques for diesel engine condition monitoring and fault diagnosis. However, they are usually used independently in practice, and limited work has been done to address the integration of data collected using the two techniques. To enhance early fault detections, a new fault diagnosis technique for the marine diesel engine has been proposed by the information fusion of the vibration and wear particle analyses in this paper. A new independent component analysis with reference algorithm (ICA-R) using the empirical mode decomposition based reference extraction scheme was adopted to identify the characteristic source signals of the engine vibration collected from multi-channel sensors. The advantage of this approach performed at a data fusion level is that the ICA-R can extract only the relevant source directly related to the engine fault features in one separation cycle via incorporating prior knowledge. The statistical values of the recovered source signals were then calculated. The above vibration features, along with the wear particle characteristics, were used as the feature vectors for the engine fault detection. Lastly, the improved simplified fuzzy ARTMAP (SFAM) was applied to integrate the distinctive features extracted from the two techniques at a decision level to detect faults in a supervised learning manner. Particularly, the immune particle swarm optimization was used to tune the vigilance parameter of the SFAM to improve the identification performance. The experimental tests were implemented on a diesel engine set-up to evaluate the effectiveness of the proposed diagnosis approach. The diagnosis results have shown that distinguished fault features can be extracted and the fault identification accuracy is satisfactory. Moreover, the fault detection rate of the integration approach has been enhanced by 16.0 % or better when compared with using the two techniques separately.

Fault-Tolerant Control for Electric Ground Vehicles With Independently-Actuated In-Wheel Motors

This paper presents an in-wheel motor fault diagnosis and fault-tolerant control method for four-wheel independently actuated (4WIA) electric vehicles. The 4WIA electric vehicle is one of the promising architectures for electric vehicles. While such a vehicle architecture greatly increases the flexibility for vehicle control, it also elevates the requirements on system reliability, safety, and fault tolerance due to the increased number of actuators. A fault diagnosis approach for finding the faulty in-wheel motor/motor driver pair is developed. The proposed diagnosis approach does not need an accurate knowledge on tire-road friction coefficient (TRFC) and is robust to tire force modeling inaccuracies. Based on the in-wheel motor/motor driver fault diagnosis mechanism, a control-allocation based vehicle fault-tolerant control system is designed to accommodate the in-wheel motor/motor driver fault by automatically allocating the control effort among other healthy wheels. Simulations using a high-fidelity, CarSim®, full-vehicle model show the effectiveness of the proposed in-wheel motor/motor driver fault diagnosis and fault-tolerant control approaches.

Dynamic Bayesian Approach for Control Loop Diagnosis with Underlying Mode Dependency

In this article, first, a hidden Markov model is built to address the temporal mode dependency problem in control loop diagnosis. A data-driven algorithm is developed to estimate the mode transition probability. The new solution to mode dependency is then further synthesized with the solution to evidence dependency to develop a recursive autoregressive hidden Markov model for online control loop diagnosis. When both the mode and evidence transition information sets are considered, the temporal information is effectively synthesized under the Bayesian framework. A simulated distillation column example and a pilot-scale experiment example are investigated to demonstrate the ability of the proposed diagnosis approach.

An Analytical Approach to Computer-Aided Diagnosis and Tuning of Lossy Microwave Coupled Resonator Filters

This paper presents a novel analytical approach to extracting of the coupling matrix of a narrow band general Chebyshev bandpass filter with losses. The approach is very useful for computer aided tuning of a microwave bandpass filter. The concept of phase loading is revealed for the first time in the community of computer aided tuning. The analytical approach consists of three elements: 1) a theoretic formula that leads to a practical scheme for determining the phase loading; 2) a theoretic formula for de-embedding the section of unknown transmission lines at the two ports of a filter; and 3) a theory for determining the unloaded Q of a filter if the loss for each resonator is nearly the same. To make the approach easy to use, some practical techniques for reconstructing rational functions of Y-parameters from a set of filter response are also provided in the paper. The proposed diagnosis approach is applicable to a general coupled resonator filter with losses and therefore can be effectively used in computer aided tuning of high order filters with cross couplings.

A new approach of simultaneous faults diagnosis based on random sets and DSmT

Simultaneous faults often occur in running equipments, in order to solve the problems of the simultaneous faults, a new approach based on random sets and Dezert-Smarandache Theory (DSmT) is proposed in this paper. Firstly, the simultaneous faults’ model is built based on the generalized frame of discernment in DSmT. Secondly, according to the unified description of combination rules in evidence reasoning based on random sets, a new combination rule for simultaneous faults diagnosis is proposed. Thirdly, according to the working characteristics and environment of the sensors used to acquire fault characteristic information, a new method to construct basic probability assignment function is proposed based on membership. Finally, diagnosis result is obtained by use of the new combination rule combined with decision rules. A case pertaining to the fault diagnosis for a multi-function rotor test-bed is given, and the result shows that the proposed diagnosis approach is feasible and efficient.

Manufacturing process diagnosis using functional regression

The quality of many manufacturing processes depends on preset process conditions and is strongly affected by process faults. Fault diagnosis is therefore crucial in modern manufacturing practice. This paper presents a new fault diagnosis approach based on the functional regression method. Fault patterns in manufacturing processes are treated as functional responses of both the preset process conditions and process faults. With these time domain functions, a mathematical model is developed, based on which a nonlinear optimization approach is applied to fault diagnosis. The proposed diagnosis approach considers the interaction effects among the faults and process settings. Therefore, it is capable of classifying multiple simultaneous faults with various process conditions. The new diagnosis method is demonstrated using an example from the resistance spot welding process. An overall success rate of 88% has been achieved.

Sensor Fault Diagnosis in State Feedback Systems using Artificial Neural Networks

A simulation before test method for fault diagnosis and classification towards sensor fault in linear time invariant state feed back system is presented in this paper. The novelty of the approach lies in associating with each state feedback gain factor a scalar , which is defined as the sensor healthiness factor. This scalar is made to vary from 1 (no fault condition) to 0 (full fault condition) in predetermined steps. The intermediate values of portray the deterioration modes of the sensor. The Integral Square Error (ISE) criterion is employed for extracting the signature of the fault and the classification is done using Artificial Neural Network (ANN) classifier. The proposed diagnosis approach is applied to a dc motor system to validate the effectiveness of the technique.program inspections, static & dynamic analysis and V&V techniques

Time-constrained failure diagnosis in distributed embedded systems: application to actuator diagnosis

Advanced automotive control applications such as steer-by-wire are typically implemented as distributed systems comprising many embedded processors, sensors, and actuators interacting via a communication bus. They have severe cost constraints, but demand a high level of safety and performance. Motivated by the need for timely diagnosis of faulty actuators in such systems, we present a method to achieve distributed failure diagnosis under deadline and resource constraints. Actuators are diagnosed in distributed fashion by processors to provide a global view of their fault status. The integration of software-based tests for actuator diagnosis within the overall control application is studied. These tests are implemented using analytical redundancy and execute concurrently with the control tasks. The test scheduling problem is then formulated and solved to guarantee actuator diagnosis within designer-specified deadlines while meeting control performance goals. As a secondary objective, the scheduling algorithm also reduces the number of processors required for diagnosis. We demonstrate the practicality of the proposed diagnosis approach by applying it to a steer-by-wire example to identify failed actuators in timely fashion.