Syed Sajjad Haider Zaidi

Time–Frequency Analysis for Efficient Fault Diagnosis and Failure Prognosis for Interior Permanent-Magnet AC Motors

The detection of noncatastrophic faults in conjunction with other factors can be used to determine the remaining life of an electric drive. As the frequency and severity of these faults increase, the working life of the drive decreases, leading to eventual failure. In this paper, four methods to identify developing electrical faults are presented and compared. They are based on the short-time Fourier transform, undecimated-wavelet analysis, and Wigner and Choi-Williams distributions of the field-oriented currents in permanent-magnet ac drives. The different fault types are classified using the linear-discriminant classifier and k-means classification. The comparison between the different methods is based on the number of correct classifications and Fishers discriminant ratio. Multiple-class discrimination analysis is also introduced to remove redundant information and minimize storage requirements.

Prognosis of Gear Failures in DC Starter Motors Using Hidden Markov Models

Diagnosis classifies the present state of operation of the equipment, and prognosis predicts the next state of operation and its remaining useful life. In this paper, a prognosis method for the gear faults in dc machines is presented. The proposed method uses the time-frequency features extracted from the motor current as machine health indicators and predicts the future state of fault severity using hidden Markov models (HMMs). Parameter training of HMMs generally needs huge historical data, which are often not available in the case of electrical machines. Methods for computing the parameters from limited data are presented. The proposed prognosis method uses matching pursuit decomposition for estimating state-transition probabilities and experimental observations for computing state-dependent observation probability distributions. The proposed method is illustrated by examples using data collected from the experimental setup.

The Effect of Failure Prognosis and Mitigation on the Reliability of Permanent-Magnet AC Motor Drives

Fault diagnosis and its extensions to failure prognosis aim to develop methods that decrease outages and thus increase reliability. Among these methods are redundancies and changes of topology, power levels, and control algorithms. Since diagnosis methods are imperfect, false positives and negatives, as well as delayed fault diagnosis, may occur. Although the diagnosis of a fault can lead to appropriate maintenance, the estimation of the time to failure through prognosis can lead to the timely mitigation of the fault and, in turn, can extend the lifetime and reliability of the drive. In this paper, we present a new approach to the mitigation of permanent-magnet ac motors that uses a failure prognosis method to predict failures and the remaining useful life and uses the output of the prognosis algorithm to decide when to modify the system and mitigate the fault. A methodology to calculate the mean time between failures with and without mitigation is also presented. The proposed approach is illustrated through a simple example of how the prognosis of failure due to a developing intermittent open circuit in one of the phases increases the drive reliability.

Cyber Security Backdrop: A SCADA testbed

Supervisory Control and Data Acquisition (SCADA) systems are traditionally proprietary and well protected. Due to increasing use of commercial/open source technology and communication protocols, there are growing concerns about the associated security threats. SCADA networks are usually employed in critical infrastructure, therefore, not much technical data of the actual systems is accessible to the research community. Most of the researchers simulate the SCADA functioning through development of testbed. Such projects are usually expensive and requiring financial sponsorship. In this paper we present a simple, inexpensive and flexible approach to develop a SCADA testbed utilizing TrueTime, a MATLAB/ Simulink based tool. The paper describes TrueTime simulation blocks, our control system, simulation of Denial of Service (DoS) attack and its effects. The main aim was to assess the effectiveness and suitability of TrueTime for the intended use in the development of a larger scale SCADA testbed. The results shown reflect that TrueTime can be effectively used for the purpose of SCADA network simulations and collection of necessary data for security analysis.

Failure prognosis of DC starter motors using hidden Markov models

This paper deals with the prognosis of gear faults in DC machines using time frequency distribution methods. The proposed method presents future state prediction of the machine faults using Hidden Markov models. Different methods for estimating the parameters of hidden Markov model with limited data are discussed. The proposed method uses Matching Pursuit decomposition and projections of the training data on linear discriminant planes for estimation of model parameters. Experimental results are presented to illustrate the method.

Prognosis of electrical faults in permanent magnet AC machines using the hidden Markov model

Reliable and fault free operation of machines needs not only timely fault detection and classification, but also an estimate of its remaining useful life, resulting in two phases of systems health monitoring, diagnosis and prognosis. Both share commonalities, with prognosis being the succeeding phase of diagnosis. In this paper, a prognosis algorithm based on the statistical hidden Markov model, is presented for the electrical faults of permanent magnet AC machines. The model parameters are computed by using the training outputs of the diagnosis phase. The algorithm estimates the failure state probability for each sampled observation. Time-frequency features extracted from the torque producing component of the machine current is used as the health indicator. The remaining useful life is estimated in terms of the probability of failure state. Parameter training of Hidden Markov Models generally need huge amounts of historical data, which are often not available in the case of highly reliable electrical machines. A method, which uses experimental observations, is presented for the computation of the state dependent observation probability densities from the limited data.

Misfire fault detection in SI engine using sliding mode observer

The novelty of work is the application of first order sliding mode techniques for hybrid model to estimate the system states which are finally analyzed to detect of Misfire fault. The output of hybrid model of SI engine (engine speed) is compared with the observed engine speed to generate the output error. The sliding mode observer is designed to track the engine speed and estimate the other system state i.e. acceleration. The crankshaft acceleration is analyzed to detect the engine misfire fault condition. The stability analysis of application of first order sliding mode for hybrid model is provided. The results of tracking of speed and estimation of acceleration using sliding mode observer are presented. The application of Sliding Mode Technique for hybrid model is first established using data generated by simulating engine model and then using experimental data taken from SI engine.

Robust droop control design for a hybrid AC/DC microgrid

We present a feedback control scheme for a hybrid bidirectional interlinking converter of an alternating current (AC)/direct current (DC) microgrid. The output voltage and current are measured which allow us to design a suitable control for the power flow. We propose a robust droop control strategy to cater for the uncertain voltage and frequency droop caused by load variations. The proposed droop controller takes into account the unmodeled load dynamics as well as the power switching transients between AC/DC microgrids. A conventional current controller followed by a sliding mode controller (SMC) are able to maintain the power stability in different operating conditions. Simulation results are presented which show performance of the proposed control scheme.

Digital configurable block based implementation of image enhancement algorithm using PSoC 5LP

In this paper four simple but very effective image enhancement technique is implemented in a digital configurable block on Programmable System on Chip (PSoC). This is done by converting a 2D gray image matrix to 1D vector and send it to PSoC5 LP Universal Asynchronous Receiver Transmitter (UART).The UART is communicating with a Verilog component which use Universal Digital Block Array (UDB) for implementing image enhancement algorithms. For implementing enhancement algorithm very simple Verilog components are developed by using Cypress PSoC Creator 2.2, It includes adder, subtracter, multiplier, 8 bit comparator, and multiplexer. Verilog components for constant values are also developed. Enhancement algorithms are implemented with the integration of arithmetic, logical and constant Verilog components.

Performance evaluation of linear and multi-linear subspace learning techniques for object classification based on underwater acoustics

Underwater object identification based on acoustic sequence is a complex task, mainly, because of the non-stationary nature of the underwater environment. Moreover, the ambient conditions contribute heavily to varying temporal and spectral characteristics of the source. Further, the characteristic features of a source lie within its spectrum whereas pure spectral contents are more robust to variations along the time and frequency axis. In this work, performance of two different class of learning approaches i.e. linear and multi-linear subspace learning, have been evaluated. Moreover, spectral features are used as inputs to both the said approaches. Two linear subspace learning techniques, namely, principal component analysis (PCA) and linear discriminant analysis (LDA) along with one multi-linear subspace learning (MSL) technique, namely, multi-linear principal component analysis (MPCA) have been used. Performance of the system was evaluated using two sets of data i.e. raw acoustic dataset having samples belonging to 4 distinct classes of ships and a synthetic dataset downloaded from DOSITS, having acoustic samples belonging to 20 distinct classes of underwater objects i.e. sea species and man-made objects. For raw acoustic database, ships signatures were collected in the Indian ocean. Further, two-pass split window (TPSW) method was used to remove background noise from the processed raw acoustic samples. For classification, two neural classifiers were used, namely, robust variable learning rate feed-forward neural network (RVLR-NN) and convolution neural network (CNN). All simulations have been conducted in MATLAB. Further, the system was evaluated under the effect of noise i.e. additive white Gaussian noise (AWGN) at different levels of signal-to-noise ratio (SNR). In addition, dimensions of the feature set were also varied and effects of dimensionality reduction on classification accuracies were observed. Simulation results observed have shown that the combination of MPCA-CNN produced best classification results with an accuracy of up to 99.4%.