Time-Domain Based Fault Detection in DC Grids

Time Domain Averaging (TDA) has been widely used for fault detection. However, it cannot reveal signal characteristics accurately in conditions of speed fluctuation and no tachometer. Empirical mode decomposition (EMD) helps to extract physically meaningful components from the singles. Dynamic Time Warping (DTW) can solve inconsistence in signal lengths per rotation due to speed fluctuation. Utilizing the advantages of EMD, DTW and TDA, an ensemble dynamic-time domain averaging (ED-TDA) algorithm is proposed for gear fault detection without tachometer. First, the selected intrinsic mode function (IMF) and the envelop signals are equal-spaced intercepted. Then, the phase accumulation error among the envelop signal segments are estimated by the DTW, which are further used to compensate the IMF segments. Finally, the compensated IMF segments are averaged to obtain the feature signal. Simulation and experimental results validate the efficiency of the algorithm in extracting feature signal from collected speed fluctuating signal without tachometer.

Download Full-text

A model-free approach to fault detection of continuous-time systems based on time domain data

International Journal of Automation and Computing ◽

10.1007/s11633-007-0189-y ◽

2007 ◽

Vol 4 (2) ◽

pp. 189-194 ◽

Cited By ~ 5

Author(s):

Ping Zhang ◽

Steven X. Ding

Keyword(s):

Fault Detection ◽

Time Domain ◽

Continuous Time ◽

Model Free ◽

Time Domain Data ◽

Continuous Time Systems ◽

Model Free Approach ◽

Time Systems

Download Full-text

Gear fault detection based on adaptive wavelet packet feature extraction and relevance vector machine

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406211400691 ◽

2011 ◽

Vol 225 (11) ◽

pp. 2727-2738 ◽

Cited By ~ 3

Author(s):

N Li ◽

C Liu ◽

C He ◽

Y Li ◽

X F Zha

Keyword(s):

Feature Extraction ◽

Fault Detection ◽

Time Domain ◽

Wavelet Packet ◽

Relevance Vector Machine ◽

Statistical Characteristics ◽

Support Vector ◽

Fisher Criterion ◽

Discrimination Power ◽

Adaptive Wavelet

In this article, a novel fault detection method based on adaptive wavelet packet feature extraction and relevance vector machine (RVM) is proposed for incipient fault detection of gear. First, ten statistical characteristics in time domain and all node energies of full wavelet packet tree are extracted as candidate features. Then, Fisher criterion is applied to evaluate the discrimination power of each feature. Finally, two optimal features from time domain and wavelet domain, respectively, are selected to be used as inputs to the RVM. Furthermore, moving average is applied to each feature to improve accuracy for online continuous fault detection. By combining wavelet packet transform with Fisher criterion, it is able to adaptively find the optimal decomposition level and select the global optimal features. The RVM, a Bayesian learning framework of statistical pattern recognition, is adopted to train the fault detection model. The RVM was compared with the popular support vector machine (SVM) with the increase of training samples. Experimental results validate the effectiveness of the proposed method, and indicate that RVM is more suitable than SVM for online fault detection.

Download Full-text

Review of Optimal Solutions to the Robustness Problem in Observer-Based Fault Detection

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1243/pime_proc_1993_207_324_02 ◽

1993 ◽

Vol 207 (2) ◽

pp. 105-112 ◽

Cited By ~ 3

Author(s):

P M Frank ◽

B Koppen

Keyword(s):

Fault Detection ◽

General Solution ◽

Frequency Domain ◽

Time Domain ◽

Fault Detection And Isolation ◽

Unknown Input ◽

Optimal Solutions ◽

Unified Approach ◽

Ultimate Objective ◽

The Time Domain

The paper presents a unified approach and general solution of the robustness problem in fault detection and isolation concepts. The ultimate objective is the design of an unknown input fault-detection observer providing a perfect decoupling between unknown inputs and faults. If this is not possible because certain prerequisites are not fulfilled, two optimal compromises in the time domain and in the frequency domain are described. The basic definitions concerning robustness and unknown input fault detectability are given, and the design techniques for the proposed approaches are outlined. The cross-connections to other methods are discussed and a practical example is given.

Download Full-text

Fault Detection of Induction Motors Using Fourier and Wavelet Analysis

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2004.p0431 ◽

2004 ◽

Vol 8 (4) ◽

pp. 431-436 ◽

Cited By ~ 3

Author(s):

Hyeon Bae ◽

◽

Youn-Tae Kim ◽

Sungshin Kim ◽

Sang-Hyuk Lee ◽

...

Keyword(s):

Neural Network ◽

Fault Detection ◽

Wavelet Analysis ◽

Time Domain ◽

Induction Motors ◽

Fuzzy Inference ◽

Classification Model ◽

Diagnosis And Prognosis ◽

Signal Features ◽

The Time Domain

The motor is the workhorse of industries. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This paper introduces fault detection for induction motors. Stator currents are measured by current meters and stored by time domain. The time domain is not suitable for representing current signals, so the frequency domain is applied to display signals. The Fourier Transform is employed to convert signals. After signal conversion, signal features must be extracted by signal processing such as wavelet and spectrum analysis. Features are entered in a pattern classification model such as a neural network model, a polynomial neural network, or a fuzzy inference model. This paper describes fault detection results that use Fourier and wavelet analysis. This combined approach is very useful and powerful for detecting signal features.

Download Full-text