Automatic Fault Diagnosis Research Articles

Side‐band algorithm for automatic wind turbine gearbox fault detection and diagnosis

Improving the availability of wind turbines is critical for minimising the cost of wind energy, especially offshore. The development of reliable and cost-effective gearbox condition monitoring systems (CMSs) is of concern to the wind industry, because the gearbox downtime has a significant effect on the wind turbine availabilities. Timely detection and diagnosis of developing gear defects is essential for minimising an unplanned downtime. One of the main limitations of most current CMSs is the time consuming and costly manual handling of large amounts of monitoring data, therefore automated algorithms would be welcome. This study presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. Based on the experimental evidence from the Durham Condition Monitoring Test Rig, a gear condition indicator was proposed to evaluate the gear damage during non-stationary load and speed operating conditions. The performance of the proposed technique was then successfully tested on signals from a full-size wind turbine gearbox that had sustained gear damage, and had been studied in a National Renewable Energy Laboratory's (NREL) programme. The results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into the wind turbine CMSs, this algorithm can automate the data interpretation, thus reducing the quantity of the information that the wind turbine operators must handle.

Research on Automatic Fault Diagnosis of Environmental Control and Life Support Systems

Research on Automatic Fault Diagnosis of Environmental Control and Life Support Systems

Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals

The objective of this paper is to provide a review of recent advances in automatic vibration- and audio-based fault diagnosis in machinery using condition monitoring strategies. It presents the most valuable techniques and results in this field and highlights the most profitable directions of research to present. Automatic fault diagnosis systems provide greater security in surveillance of strategic infrastructures, such as electrical substations and industrial scenarios, reduce downtime of machines, decrease maintenance costs, and avoid accidents which may have devastating consequences. Automatic fault diagnosis systems include signal acquisition, signal processing, decision support, and fault diagnosis. The paper includes a comprehensive bibliography of more than 100 selected references which can be used by researchers working in this field.

Design and Implementation of Universal Detection Equipment of Inertial Navigation System Based on "Platform + Adapter Structure"

According to such problems as complicated structure, too many circuit module and online difficult detection of vehicle-mounted platform inertial navigation system, the detector adopts the detection mode of platform + adapter to realize the universalization and modularization of the detection equipment. It reduced circuit scale and the difficulty of design, strengthened systems scalability, at the same time made equipment carry and use more flexible. It realized inertial navigation board level to device level detection by using intelligent fault diagnosis technology to automatic fault diagnosis of circuit. Meanwhile, with the effective optimization of lesser detection procedures, the high efficiency and accurateness of fault isolation and location had been guaranteed, detection equipments automation, integrated, accuracy greatly raised, and detection time greatly shortened.

Diagnosis of Nuclear Power Plant Based on Probabilistic Neural Network

This paper presents development of an automatic fault diagnosis system in the nuclear power plants to minimize possible nuclear disasters. Probabilistic Neural Network (PNN) overcame the shortcomings of entrapment in local optimum, slow convergence rate which was in BP algorithm. PNN is fit to diagnose the fault of nuclear power plant and has auto-adaptability. The method not only makes the original neural network smaller in terms of computation and realization, but also improves diagnosis speed and accuracy of nuclear power plant.

Analysis of the Automatic Transmission Fault Diagnosis Based on Bayesian Network Sensitivity

Automobile automatic transmission is a complex system integrates machine, electricity, liquid, in terms of its fault diagnosis has the characteristics of uncertainty and high complex correlation, thus put forward the Bayesian network based on the theory of probability and theory of the fault tree for fault diagnosis methods of automobile automatic transmission, it has a certain value for application. Take a failure automatic transmission as an example, analyzes the causes of the automatic transmission fault and the relationship between the affecting factors, according to the properties of the automatic transmission fault establishes a Bayesian network model, and through calculating sensitivity to determine the probability of a certain causes to the automatic transmission fault. The results show that the method is operable in automatic transmission fault diagnosis, diagnosis results are accurate and credible.

Building Fan Coil Electric Consumption Analysis with Fuzzy Approaches for Fault Detection and Diagnosis

In the building energy efficiency field, developing automatic and accurate fault detection and diagnosis methods is necessary in order to ensure optimal operations of systems and to save energy. In this paper first, fault detection analysis based on statistical methods where anomalies are detected through a comparison with neighborhood and averaged fault-free values and through a clustering technique is performed. Following the fault detection step, a fault diagnosis analysis based on fuzzy sets and fuzzy logic is implemented. Experimentation is carried out over a one day monitoring data set in December 2013 for the fan coil electric consumption of an actual office building located at ENEA ‘Casaccia’ Research Centre. Results show the effectiveness of proposed approaches in automatic detection and diagnosis of abnormal building fan coil electric consumption.

Application of order cyclostationary demodulation to damage detection in a direct-driven wind turbine bearing

This paper presents a method of fault detection and isolation for a direct-driven wind turbine (DWT) bearing. Computed order tracking is employed to convert the non-stationary envelope signal in the time domain into a quasi-stationary signal in the angular domain by even-angle resampling. Cyclostationary demodulation is then utilized to expose the orders related to fault characteristics in the demodulation spectrum. In order to realize the automatic fault diagnosis and emit a stable alarm about bearing damage, the peak value of the demodulation spectrum is scaled and compared to a defined threshold. The significant advantage of the proposed method is the implementation of an automatic algorithm for DWT bearing diagnostics under randomly varying speed and highly alternating load. Practical applications are provided to show that the proposed approach is able to achieve reliable failure warning in the bearing condition monitoring of a DWT.

An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space

Although reconstructed phase space is one of the most powerful methods for analyzing a time series, it can fail in fault diagnosis of an induction motor when the appropriate pre-processing is not performed. Therefore, boundary analysis based a new feature extraction method in phase space is proposed for diagnosis of induction motor faults. The proposed approach requires the measurement of one phase current signal to construct the phase space representation. Each phase space is converted into an image, and the boundary of each image is extracted by a boundary detection algorithm. A fuzzy decision tree has been designed to detect broken rotor bars and broken connector faults. The results indicate that the proposed approach has a higher recognition rate than other methods on the same dataset.

Automatic fault diagnosis in PV systems with distributed MPPT

This work presents a novel procedure for fault diagnosis in PV systems with distributed maximum power point tracking at module level—power optimizers (DC/DC) or micro-inverters (DC/AC). Apart from the power benefits obtained when an irregular irradiance distribution is present, this type of systems permit the monitoring of the PV plant parameters at the module level: voltage and current at the working power point. With these parameters, a prototype diagnosis tool has been developed in Matlab and it has been experimentally verified in a real rooftop PV generator by applying different failures. The tool can diagnose the following failures: fixed object shading (with distance estimation), localized dirt, generalized dirt, possible hot-spots, module degradation and excessive losses in DC cables. In addition, it alerts the user of the power losses produced by each failure and classifies the failures by their severity. This system does not require the use of irradiance or temperature sensors, except for the generalized dirt failure, reducing the cost of installation, especially important in small PV systems.

Localisation of multiple faults in motorcycles based on the wavelet packet analysis of the produced sounds

Service station experts examine the sound patterns of the motorcycles to diagnose the faults. Automatic fault diagnosis is a challenging task and more so is recognition of multiple faults. This study presents a methodology for localisation of multiple faults in motorcycles. The sound signatures of multiple faults are constructed by fusing the individual signatures of faults from engine and exhaust subsystems. Energy distributions in the approximation coefficients of wavelet packets are used as features. Among the classifiers used, artificial neural network is found suitable for detecting the presence of multiple faults. The recognition accuracy is over 78% when trained with individual fault signatures and over 88% when trained with combined fault signatures.

Automaton Fault Diagnosis Based on Chaos Theory

The work of the automaton fault diagnosis based on experimental measurement has not been researched except the fund project which this paper was come from. First, study the structure characteristics, movement process and several common failure modes of the automaton. Then we combined the automaton movement process analysis and the nonlinear, short-term impact characteristics of the vibration signal, proposed for the first time with a powerful tool for nonlinear problem researchthe chaos theory, to research the fault diagnosis of automaton, and finally realized it. It provided a new way for the fault diagnosis of automatic weapons. That has important theoretical and practical significance for high speed automatic fault diagnosis.

Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier

The rolling element bearing is among the most frequently encountered component in a rotating machine. Bearing fault can cause machinery breakdown and lead to productivity loss. A bearing fault diagnosis method has been proposed based on multi-scale permutation entropy (MPE) and adaptive neuro fuzzy classifier (ANFC). In this paper, MPE is applied for feature extraction to reduce the complexity of the feature vector. Extracted features are given input to the ANFC for an automated fault diagnosis procedure. Vibration signals are captured for healthy and faulty bearings. Experiment results pointed out that proposed method is a reliable approach for automated fault diagnosis. Thus, this approach has potential in diagnosis of incipient bearing faults.

Automated Fault Diagnosis for an Autonomous Underwater Vehicle

This paper reports our results in using a discrete fault diagnosis system Livingstone 2 (L2), onboard an autonomous underwater vehicle (AUV) Autosub 6000. Due to the difficulty of communicating between an AUV and its operators, AUVs can benefit particularly from increased autonomy, of which fault diagnosis is a part. However, they are also restricted in their power consumption. We show that a discrete diagnosis system can detect and identify a number of faults that would threaten the health of an AUV, while also being sufficiently lightweight computationally to be deployed onboard the vehicle. Since AUVs also often have their missions designed just before deployment in response to data from previous missions, a diagnosis system that monitors the software as well as the hardware of the system is also very useful. We show how a software diagnosis model can be built automatically that can be integrated with the hardware model to diagnose the complete system. We show empirically that on Autosub 6000 this allows us to diagnose real vehicle faults that could potentially lead to the loss of the vehicle.

English

Vehicles produce sound signals with varying temporal and spectral properties under different working conditions. These sounds are indicative of the condition of the engine. Fault diagnosis is a significantly difficult task in geographically remote places where expertise is scarce. Automated fault diagnosis can assist riders to assess the health condition of their vehicles. This paper presents a method for fault detection and location in motorcycles based on the chain code of the pseudospectra and Mel-frequency cepstral coefficient (MFCC) features of acoustic signals. The work comprises two stages: fault detection and fault location. The fault detection stage uses the chain code of the pseudospectrum as a feature vector. If the motorcycle is identified as faulty, the MFCCs of the same sample are computed and used as features for fault location. Both stages employ dynamic time warping for the classification of faults. Five types of faults in motorcycles are considered in this work. Observed classification rates are over 90% for the fault detection stage and over 94% for the fault location stage. The work identifies other interesting applications in the development of acoustic fingerprints for fault diagnosis of machinery, tuning of musical instruments, medical diagnosis, etc.

A Framework Design of Automatic Fault Diagnosis System

To resolve a lasting suitable cabin environment for the astronauts, this paper proposes an effective framework design for automatic fault diagnosis system. This framework can implement a real-time online diagnosis and decision support for fault, and carry out an early diagnosis for weak fault. Finally, this paper achieves an online automatic fault diagnosis system by using neural networks self-learning characteristics and expert knowledge. In two-men-two-days simulated manned space flight test, the software of diagnosis system framework worked well, which has been assessed and verified comprehensively.

Automatic fault diagnosis of rotating machines by time-scale manifold ridge analysis

This paper explores the improved time-scale representation by considering the non-linear property for effectively identifying rotating machine faults in the time-scale domain. A new time-scale signature, called time-scale manifold (TSM), is proposed in this study through combining phase space reconstruction (PSR), continuous wavelet transform (CWT), and manifold learning. For the TSM generation, an optimal scale band is selected to eliminate the influence of unconcerned scale components, and the noise in the selected band is suppressed by manifold learning to highlight the inherent non-linear structure of faulty impacts. The TSM reserves the non-stationary information and reveals the non-linear structure of the fault pattern, with the merits of noise suppression and resolution improvement. The TSM ridge is further extracted by seeking the ridge with energy concentration lying on the TSM signature. It inherits the advantages of both the TSM and ridge analysis, and hence is beneficial to demodulation of the fault information. Through analyzing the instantaneous amplitude (IA) of the TSM ridge, in which the noise is nearly not contained, the fault characteristic frequency can be exactly identified. The whole process of the proposed fault diagnosis scheme is automatic, and its effectiveness has been verified by means of typical faulty vibration/acoustic signals from a gearbox and bearings. A reliable performance of the new method is validated in comparison with traditional enveloping methods for rotating machine fault diagnosis.

Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory

The present study proposes a methodology for the automatic diagnosis of short-circuit faults in distribution systems using modern techniques for signal analysis and artificial intelligence. This support tool for decision making accelerates the restoration process, providing greater security, reliability and profitability to utilities. The fault detection procedure is performed using statistical and direct analyses of the current waveforms in the wavelet domain. Current and voltage signal features are extracted using discrete wavelet transform, multi-resolution analysis and energy concept. These behavioural indices correspond to the input vectors of three parallel sets of fuzzy ARTMAP neural networks. The network outcomes are integrated by the Dempster-Shafer theory, giving quantitative information about the diagnosis and its reliability. Tests were carried out using a practical distribution feeder from a Brazilian electric utility, and the results show that the method is efficient with a high level of confidence.

Artificial Neural Network Based Fault Diagnosis of IC Engines

Fault diagnosis is important to avoid unforeseen failures of IC engines, but normally requires an expert to interpret analysis results. Artificial Neural Networks are potential tools for the automated fault diagnosis of IC engines, as they can learn the patterns corresponding to various faults. Most engine faults can be classified into two categories: combustion faults and mechanical faults. Misfire is a typical combustion fault; piston slap and big end bearing knock are common mechanical faults. The automated diagnostic system proposed in this paper has three main stages, each stage including three neural networks. The first stage is the fault detection stage, where the neural networks detect whether there are faults in the engine and if so which kind. In the second stage, based on the detection results, the severity of the faults was identified. In the third stage, the neural networks localize which cylinder has a fault. The critical thing for a neural network is its input feature vector, and a previous study had indicated a number of features that should differentiate between the different faults and their location, based on advanced signal processing of the vibration signals measured for different normal and fault conditions. In this study, an advanced feature selection technology was employed to select the significant features as the inputs to networks. The input vectors were separated into two groups, one for training the network, and the other for its validation. Finally it has been demonstrated that the neural network based system can automatically differentiate and diagnose a number of engine faults, including location and severity.

The Design of Welding Control System for Intersection of T-Tube's Seam Welding Based on the Multi-Axis

To improve the quality and efficiency of T-shaped pipe welding, a double-gun welding CNC is designed based on Multi-axis Control. The control program using Delphi software can accomplish system management and control functions. This system could display current status of each axis in real-time, complete automatic fault diagnosis, modify the welding parameters and so on.