Induction Machine Stator Fault Tracking using the Growing Curvilinear Component Analysis

Detection of stator-based faults in Induction Machines (IMs) can be carried out in numerous ways. In particular, the shorted turns in stator windings of IM are among the most common faults in the industry. As a matter of fact, most IMs come with pre-installed current sensors for the purpose of control and protection. At this aim, using only the stator current for fault detection has become a recent trend nowadays as it is much cheaper than installing additional sensors. The three-phase stator current signatures have been used in this study to observe the effect of stator inter-turn fault with respect to the healthy condition of the IM. The pre-processing of the healthy and faulty current signatures has been done via the in-built DSP module of dSPACE after which, these current signatures are passed into the MATLAB® software for further analysis using AI techniques. The authors present a Growing Curvilinear Component Analysis (GCCA) neural network that is capable of detecting and follow the evolution of the stator fault using the stator current signature, making online fault detection possible. For this purpose, a topological manifold analysis is carried out to study the fault evolution, which is a fundamental step for calibrating the GCCA neural network. The effectiveness of the proposed method has been verified experimentally

Gas turbine and sensor fault diagnosis with nested artificial neural networks

Accurate gas turbine diagnosis relies on accurate measurements from sensors. Unfortunately, sensors are prone to degradation or failure during gas turbine operations. In this paper a stack of decentralised artificial neural networks are introduced and investigated as an approach to approximate the measurement of a failed sensor once it is detected. Such a system is embedded into a nested neural network system for gas turbine diagnosis. The whole neural network diagnostic system consists of a number of feedforward neural networks for engine component diagnosis, sensor fault detection and isolation; and a stack of decentralised neural networks for sensor fault recovery. The application of the decentralised neural networks for the recovery of any failed sensor has the advantage that the configuration of the nested neural network system for engine component diagnosis is relatively simple as the system does not take into account sensor failure. When a sensor fails, the biased measurement of the failed sensor is replaced with a recovered measurement approximated with the measurements of other healthy sensors. The developed approach has been applied to an engine similar to the industrial 2-shaft engine, GE LM2500+, whose performance and training samples are simulated with an aero-thermodynamic modelling tool — Cranfield University’s TURBOMATCH computer program. Analysis shows that the use of the stack of decentralised neural networks for sensor fault recovery can effectively recover the measurement of a failed sensor. Comparison between the performance of the diagnostic system with and without the decentralised neural networks shows that the sensor recovery can improve the performance of the neural network engine diagnostic system significantly when a sensor fault is present. Copyright © 2004 by ASM

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

The use of artificial neural network for low latency of fault detection and localisation in transmission line

One of the most critical concerns in power system reliability is the timely and accurate detection of transmission line faults. Therefore, accurate detection and localisation of these faults are necessary to avert system collapse. This paper focuses on using Artificial Neural Networks in faults detection and localisation to attain accuracy, precision and speed of execution. A 330 kV, 500 km three-phase transmission line was modelled to extract faulty current and voltage data from the line. The Artificial Neural Network technique was used to train this data, and an accuracy of 100% was attained for fault detection and about 99.5% for fault localisation at different distances with 0.0017 μs of detection and an average error of 0%–0.5%. This model performs better than Support Vector Machine and Principal Component Analysis with a higher fault detection time. This proposed model serves as the basis for transmission line fault protection and management system

Air vehicle diagnostic system rotor head load and global fault evaluation as installed on the SH-60F helicopter

This study covers SH-60F rotor head load estimation, track and balance fault detection, and degraded component diagnostics of the Air Vehicle Diagnostic SystemAdvanced Technology Demonstration. The purpose of this study was to acquire load data from selected,SH-60F main rotor components; provide proof of concept for the use of neural networks in load estimation; acquire SH-60F main rotor track and balance and vibration data in nominal (no known defect) condition and with intentionally induced known out of track and balance conditions in the main rotor; and acquire SH-60F main rotor track and balance and vibration data in nominal condition and with degraded rotor head components at varying flight conditions. The method of data collection was a survey recorder which houses a memory unit, a neural network and the maneuver recognition algorithms. The memory unit was used to record the load estimation,maneuver recognition algorithms, track and balance, and main rotor degraded component detection system results. Data signals are received directly from the aircraft 1553 databus, existing aircraft systems, and special sensors designed for each task. Strain gauges placed at four different rotor head locations to provide truth data for comparison with the neural network data. The instrumentation monitors 37 parameters for three testing phases totaling over 50 flight hours. Results indicate that the neural network estimation are within 10 percent of the actual strain gauge loads and that the main rotor track and balance capability of the system was able to quickly and accurately track the rotor system and lower vibrations to acceptable levels. The main rotor head fault detection data provided further support for the use of neural networks in vibration analysis

Applied sensor fault detection, identification and data reconstruction based on PCA and SOMNN for industrial systems

The paper presents two readily implementable approaches for Sensor Fault Detection, Identification (SFD/I) and faulted sensor data reconstruction in complex systems, in real-time. Specifically, Principal Component Analysis (PCA) and Self-Organizing Map Neural Networks (SOMNNs) are demonstrated for use on industrial turbine systems. In the first approach, Squared Prediction Error (SPE) based on the PCA residual space is used for SFD. SPE contribution plot is employed for SFI. A missing value approach from an extension of PCA is applied for faulted sensor data reconstruction. In the second approach, SFD is performed by SOMNN based Estimation Error (EE), and SFI is achieved by EE contribution plot. Data reconstruction is based on an extension of the SOMNN algorithm. The results are compared in each examining stage. The validation of both approaches is demonstrated through experimental data during the commissioning of an industrial 15MW turbine

Using Neural Networks for Sensor Validation

This paper presents the results of applying two different types of neural networks in two different approaches to the sensor validation problem. The first approach uses a functional approximation neural network as part of a nonlinear observer in a model-based approach to analytical redundancy. The second approach uses an auto-associative neural network to perform nonlinear principal component analysis on a set of redundant sensors to provide an estimate for a single failed sensor. The approaches are demonstrated using a nonlinear simulation of a turbofan engine. The fault detection and sensor estimation results are presented and the training of the auto-associative neural network to provide sensor estimates is discussed

Fault Detection and Isolation In Gas Turbine Engines

Aircraft engines are complex systems that require high reliability and adequate monitoring to ensure flight safety and performance. Moreover, timely maintenance has necessitated the need for intelligent capabilities and functionalities for detection and diagnosis of anomalies and faults. In this thesis, fault diagnosis in aircraft jet engines is investigated by using intelligent-based methodologies. Two different artificial neural network schemes are introduced for this purpose. The first fault detection and isolation (FDI) scheme for an aircraft jet engine is based on the multiple model approach and utilizes dynamic neural networks (DNN). Towards this end, multiple DNNs are constructed to learn the nonlinear dynamics of the aircraft jet engine. Each DNN represents a specific operating mode of the healthy or the faulty conditions of the jet engine. The inherent challenges in fault diagnosis systems is that their performance could be excessively reduced under sensor fault and sensor degradation conditions (such as drift and noise). This thesis proposes the use of data validation and sensor fault detection to improve the performance of the overall fault diagnosis system. In this regard the concept of nonlinear principle components analysis (NPCA) is exploited by using autoassociative neural networks. The second FDI scheme is developed by using autoassociative neural networks (ANN). A parallel bank of ANNs are proposed to diagnose sensor faults as well as component faults in the aircraft jet engine. Unlike most FDI techniques, the proposed solution simultaneously accomplishes sensor faults and component faults detection and isolation (FDI) within a unified diagnostic framework. In both proposed FDI approaches, by using the residuals that are generated from the difference between each network output and the measured jet engine output as well as selection of a proper threshold for each network, criteria are established for performing the fault diagnosis of the jet engines. The fault diagnosis tasks consists of determining the time as well as the location of a fault occurrence subject to the presence of disturbances and measurement noise. Simulation results presented, demonstrate and illustrate the effective performance of our proposed neural network-based FDI strategies

Tracking Evolution of Stator-based Fault in Induction Machines using the Growing Curvilinear Component Analysis Neural Network

Stator-based faults are one of the most common faults among induction motors (IMs). The conventional approach to IM control and protection employs current sensors installed on the motor. Recently, most studies have focused on fault detection by means of stator current. This paper presents an application of the Growing Curvilinear Component Analysis (GCCA) neural network aided by the Extended Park Vector Approach (EPVA) for the purpose of transforming the three-phase current signals. The GCCA is a growing neural based technique specifically designed to detect and follow changes in the input distribution, e.g. stator faults. In particular, the GCCA has proven its capability of correctly identifying and tracking stator inter-turn fault in IMs. To this purpose, the three-phase stator currents have been acquired from IMs, which start at healthy operating state and, evolve to different fault severities (up to 10%) under different loading conditions. Data has been transformed using the EPVA and pre-processed to extract statistical time domain features. To calibrate the GCCA neural network, a topological manifold analysis has been carried out to study the input features. The efficacy of the proposed method has been verified experimentally using IM with l.lkW rating and has potential for IMs with different manufacturing conditions