AU2021104319A4 - A system for traction inverter fault detection and a method thereof - Google Patents
A system for traction inverter fault detection and a method thereof Download PDFInfo
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- AU2021104319A4 AU2021104319A4 AU2021104319A AU2021104319A AU2021104319A4 AU 2021104319 A4 AU2021104319 A4 AU 2021104319A4 AU 2021104319 A AU2021104319 A AU 2021104319A AU 2021104319 A AU2021104319 A AU 2021104319A AU 2021104319 A4 AU2021104319 A4 AU 2021104319A4
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- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000001514 detection method Methods 0.000 title description 3
- 238000000605 extraction Methods 0.000 claims abstract description 34
- 238000007781 pre-processing Methods 0.000 claims abstract description 34
- 239000000284 extract Substances 0.000 claims description 4
- 238000012549 training Methods 0.000 claims description 4
- 239000004020 conductor Substances 0.000 claims 1
- 230000007257 malfunction Effects 0.000 claims 1
- 238000005382 thermal cycling Methods 0.000 claims 1
- 230000008901 benefit Effects 0.000 description 9
- 230000008569 process Effects 0.000 description 9
- 238000004422 calculation algorithm Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 5
- 238000003745 diagnosis Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000003595 spectral effect Effects 0.000 description 3
- 238000007418 data mining Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000012952 Resampling Methods 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000001174 ascending effect Effects 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 238000013145 classification model Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000012517 data analytics Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000007477 logistic regression Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
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- 238000012706 support-vector machine Methods 0.000 description 1
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Classifications
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- Automation & Control Theory (AREA)
- Testing Of Short-Circuits, Discontinuities, Leakage, Or Incorrect Line Connections (AREA)
Abstract
A system (100) for diagnosing fault in an electronic device (102),
comprising an input module (104) comprising of a plurality of signals
using a current sensor (114); at least two signal preprocessing
modules (106a, 106b) for improving the quality of signals using a
welch method; at least two feature extraction module (108a, 108b) to
identify the fault in the electronic device using find peaks function; a
classification module (110) to detect presence of fault using a K
Nearest neighbor (K-NN) classifier, wherein a zero value is returned
when no fault is detected, else one is returned; and a prediction
module (112) for identifying the category of fault using at least three
fault indices, wherein a first fault indices is activated on an open circuit
fault, a second fault indices is activated on a short circuit fault, and a
third fault indices is activated on a faulty sensor and healthy electronic
device condition.
18
inal Signal Feature
~~3Preprocessing Extractiori
LnkoM11 dt Signal Feature -- riito
Preprocessing Extraction Peito
1ndicos
FIGURE 3
Description
inal Signal Feature ~~3Preprocessing Extractiori
LnkoM11 dt Signal Feature -- riito Preprocessing Extraction Peito
1ndicos
FIGURE 3
FIELD OFINVENTION The present invention generally relates to a field of fault detection systems. More particularly, the present invention relates to a system and a method for detecting and diagnosing faults of an electrical or electronic component such as an inverter based on sensor data inputs.
BACKGROUND OF THE INVENTION Electric vehicles are emerging as a pivotal solution to one of the energy future's tremendous hurdles, which is the influence of transportation on the environment. This new model of mobility should be secure, trustworthy, and convenient to accomplish the thermal vehicle.
Nevertheless, a fully electric vehicle was constructed around vulnerable electrical components, the introduction of a simplistic defect in one of the elements that form the electric powertrain can lead to unacceptable outcomes on the intact system. To build an electric vehicle that meets the level of safety required by ISO 26262 standards, it is necessary to integrate the fault diagnosis algorithm in the control law.
The main drawbacks of the existing systems is that the kind of monitoring control is designed in the most state with a determined threshold, which can lead to a wrong alarm. In other words, if the threshold is high, it may lead to wasted detections, the same case for low threshold; it may derive a false alarm. Hence, the threshold necessitates being adaptively tuned.
In order to overcome the above-mentioned limitations, there exists a need to develop a system that diagnosis fault of the traction inverter using machine learning for accurate diagnosis based on estimating power spectral density and generate fault indices without the use of threshold.
The technical advancements disclosed by the present invention overcomes the limitations and disadvantages of existing and convention systems and methods.
The present invention generally relates to a system and a method for diagnosing fault in traction inverter.
An object of the present invention is to perform diagnosis of a fault.
Another object of the present invention is to locate the deficiencies.
Another object of the present invention is to find multiple faults occurring in a system.
According to an aspect of the present disclosure, a system for diagnosing fault in the traction inverter, wherein the system comprises of:
The input module comprising of a plurality of signals obtained from the electronic device using a current sensor, wherein the current sensor measures current flowing in the electronic device, wherein the input module collects signals from the current sensor and from an unknown data. The unknown data represents the phase current under load variation.
The two signal preprocessing module each connected to the unknown data source and the current sensor for improving the quality of signals received, wherein the preprocessing module uses welch method for preprocessing the received signals and converges the signal.
The two feature extraction module connected to each of the two-signal preprocessing module for extracting the preprocessed signals, wherein the feature extraction module extracts the features of the preprocessed signals to identify the fault in the electronic device using findpeaks function.
The classification module is connected to the feature extraction module for classifying the extracted features to detect presence of fault occurring in the electronic device, wherein the classification module uses a K-Nearest neighbor (K-NN) classifier to train the obtained extracted features, wherein when the extracted features are trained, a zero value is returned by the classifier when no fault is detected, else a value one is returned.
The prediction module is connected to the classification module and the feature extraction module of the unknown data source for identifying the category of fault using at least three fault indices such as Fl, F2 and F3, wherein F1 fault indices is activated when an open circuit fault occurs, wherein F2 fault indices is activated when a short circuit fault occurs, wherein F3 fault indices is activated when a faulty sensor and healthy electronic device condition.
According to an aspect of the present disclosure, the process for diagnosing fault in an electronic device, the method comprises of:
The first step discloses about obtaining a plurality of signals from the electronic device using a current sensor associated to an input module, wherein measuring current flowing in the electronic device using the current sensor, wherein collecting signals from the current sensor and from an unknown data using the input module.
The second Step discloses about improving the quality of signals received using at least two signal preprocessing module each connected to the unknown data source and the current sensor, wherein preprocessing the received signals and converges the signals using welch method of the preprocessing module.
The third Step discloses about extracting the preprocessed signals using at least feature extraction module connected to each of the two signal preprocessing module, wherein identifying the fault in the electronic device using findpeaks function of the feature extraction module.
The fourth Step discloses about classifying the extracted features to detect presence of fault occurring in the electronic device using a classification module connected to the feature extraction module, wherein training the obtained extracted features using the classification module consisting of a K-Nearest neighbor (K-NN) classifier, wherein when the extracted features are trained, a zero value is returned by the classifier when no fault is detected, else a value one is returned.
The fifth Step discloses about identifying the category of fault using at least three fault indices such as Fl, F2 and F3 using a prediction module connected to the classification module and the feature extraction module of the unknown data source, wherein F1 fault indices is activated when an open circuit fault occurs, wherein F2 fault indices is activated when a short circuit fault occurs, wherein F3 fault indices is activated when a faulty sensor and healthy electronic device condition.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
Figure 1 illustrates a block diagram of a system for diagnosing fault in an electronic device,
Figure 2 illustrates a flow diagram of the method for diagnosing fault in an electronic device, and
Figure 3 illustrates a working block diagram of the system for diagnosing fault in the traction inverter.
Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have been necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.
For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.
It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.
Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
Embodiments of the present invention will be described below in detail with reference to the accompanying drawings.
Figure 1 illustrates a block diagram of a system (100) for diagnosing fault in an electronic device (102). The system (100) comprises of an input module (104), at least two signal processing module (106a, 106b), at least two feature extraction module (108a, 108b), a classification module (110), and a prediction module (112).
The input module (104) comprising of a plurality of signals obtained from the electronic device (102) using a current sensor (114), wherein the current sensor (114) measures current flowing in the electronic device (102) which is a traction inverter, wherein the input module (104) collects signals from the current sensor (114) and from an unknown data.
The current sensor (114) is a module that detects electric current in a wire and generates a signal proportional to that current. The generated signal could be analog voltage or current or a digital output. The generated signal can be then used to display the measured current in an ammeter, or can be stored for further analysis in an input module (104), or can be used for the purpose of detecting faults in the inverter.
The two signal preprocessing module (106a, 106b) each connected to the unknown data source and the current sensor (114) for improving the quality of signals received, wherein the preprocessing module (106a, 106b) uses welch method for preprocessing the received signals and converges the signals.
The preprocessing module (106a, 106b) includes artifact removal, noise filtering, and resampling the signal to comply with current sensor (114) input specifications. A low pass filter along with an artifact removal algorithm using adaptive signal processing techniques are used.
The signal preprocessing module (106a, 106b) technique includes Time-frequency analysis for processing non-stationary signals and Spectral estimation - for determining the spectral content (i.e., the distribution of power over frequency) of a time series.
The Welch's method is used specifically for estimating power spectra and is carried out by dividing the time signal obtained by the preprocessing module (106a, 106b) into successive blocks, forming a periodogram for each block, and averaging. The periodograms are formed from non-overlapping successive blocks of data.
The two feature extraction module (108a, 108b) is connected to each of the two-signal preprocessing module (106a, 106b) for extracting the preprocessed signals, wherein the feature extraction module (108a, 108b) extracts the features of the preprocessed signals to identify the fault in the electronic device (102) using findpeaks function.
The feature extraction module (108a, 108b) is a process of dimensionality reduction by which an initial set preprocessed signal and is reduced to more manageable groups for processing. A characteristic of these large data sets is a large number of variables that require a lot of computing resources to process.
The techniques used for feature extraction are time frequency distributions (TFD), fast Fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on.
The classification module (110) connected to the feature extraction module (108a, 108b) for classifying the extracted features to detect presence of fault occurring in the electronic device (102), wherein the classification module (110) uses a K-Nearest neighbor (K-NN) classifier to train the obtained extracted features, wherein when the extracted features are trained, a zero value is returned by the classifier when no fault is detected, else a value one is returned.
The Classification is a process of categorizing a given set of extracted features obtained from the input module (104) into a plurality of classes. The classification is performed on both structured or unstructured data. The process starts with predicting the class of given data points. The classes are often referred to as target, label or categories. The different types of classification techniques are Logistic Regression, NaYve Bayes, Stochastic Gradient Descent, K-Nearest Neighbors, Decision Tree, Random Forest and Support Vector Machine.
The classification technique used in the present invention is K-NN algorithm. KNN algorithm is one of the simplest classification algorithms and it is one of the most used learning algorithms. KNN is a non-parametric, lazy learning algorithm. Its purpose is to use a database in which the data points are separated into several classes to predict the classification of a new sample point. The K-NN works by:
a) Calculating the distance between test data and each row of training data. b) Sort the calculated distances in ascending order based on distance values. c) Get top k rows from the sorted array. d) Get the most frequent class of these rows. e) Return the predicted class.
The prediction module (112) is connected to the classification module (110) and the feature extraction module (108a, 108b) of the unknown data source for identifying the category of fault using at least three fault indices such as Fl, F2 and F3, wherein F1 fault indices is activated when an open circuit fault occurs, wherein F2 fault indices is activated when a short circuit fault occurs, wherein F3 fault indices is activated when a faulty sensor and healthy electronic device (102) condition.
The Predictive module is the subpart of data analytics that uses data mining and probability to predict fault. Each model is built up by the number of predictors that are highly favorable to determine future decisions. Once the data is received for a specific predictor, an analytical model is formulated.
There are two types of predictive models. They are Classification models, that predict class membership, and Regression models that predict a number. These models are then made up of algorithms. The algorithms perform the data mining and statistical analysis, determining trends and patterns in data.
Figure 2 illustrates a flow diagram of the process for diagnosing fault in an electronic device (102), the method comprises of:
Step (202) discloses about obtaining a plurality of signals from the electronic device (102) using a current sensor (114) associated to an input module (104), wherein measuring current flowing in the electronic device (102) using the current sensor (114), wherein collecting signals from the current sensor (114) and from an unknown data using the input module (104).
Step (204) discloses about improving the quality of signals received using at least two signal preprocessing module (106a, 106b) each connected to the unknown data source and the current sensor (114), wherein preprocessing the received signals and converges the signals using welch method of the preprocessing module (106a, 106b).
Step (206) discloses about extracting the preprocessed signals using at least feature extraction module (108a, 108b) connected to each of the two-signal preprocessing module (106a, 106b), wherein identifying the fault in the electronic device (102) using findpeaks function of the feature extraction module (108a, 108b).
Step (208) discloses about classifying the extracted features to detect presence of fault occurring in the electronic device (102) using a classification module (110) connected to the feature extraction module (108a, 108b), wherein training the obtained extracted features using the classification module (110) consisting of a K-Nearest neighbor (K NN) classifier, wherein when the extracted features are trained, a zero value is returned by the classifier when no fault is detected, else a value one is returned.
Step (210) discloses about identifying the category of fault using at least three fault indices such as Fl, F2 and F3 using a prediction module (112) connected to the classification module (110) and the feature extraction module (108a, 108b) of the unknown data source, wherein F1 fault indices is activated when an open circuit fault occurs, wherein F2 fault indices is activated when a short circuit fault occurs, wherein F3 fault indices is activated when a faulty sensor and healthy electronic device (102) condition.
Figure 3 discloses about a block diagram of the system (100) for diagnosing fault in the traction inverter (102), wherein the system (100) comprises of:
The input module (104) comprising of a plurality of signals obtained from the electronic device (102) using a current sensor (114), wherein the current sensor (114) measures current flowing in the electronic device (102), wherein the input module (104) collects signals from the current sensor (114) and from an unknown data.
The two signal preprocessing module (106a, 106b) each connected to the unknown data source and the current sensor (114) for improving the quality of signals received, wherein the preprocessing module (106a, 106b) uses welch method for preprocessing the received signals and converges the signal.
The two feature extraction module (108a, 108b) connected to each of the two-signal preprocessing module (106a, 106b) for extracting the preprocessed signals, wherein the feature extraction module (108a, 108b) extracts the features of the preprocessed signals to identify the fault in the electronic device (102) using findpeaks function.
The classification module (110) is connected to the feature extraction module (108a, 108b) for classifying the extracted features to detect presence of fault occurring in the electronic device (102), wherein the classification module (110) uses a K-Nearest neighbor (K-NN) classifier to train the obtained extracted features, wherein when the extracted features are trained, a zero value is returned by the classifier when no fault is detected, else a value one is returned.
The prediction module (112) is connected to the classification module (110) and the feature extraction module (108a, 108b) of the unknown data source for identifying the category of fault using at least three fault indices such as Fl, F2 and F3, wherein F1 fault indices is activated when an open circuit fault occurs, wherein F2 fault indices is activated when a short circuit fault occurs, wherein F3 fault indices is activated when a faulty sensor and healthy electronic device (102) condition.
The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.
Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.
Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.
Claims (5)
1. A system (100) for diagnosing fault in an electronic device (102), wherein the system (100) comprises of:
an input module (104) comprising of a plurality of signals obtained from the electronic device using a current sensor (114), wherein the current sensor (114) measures current flowing in the electronic device, wherein the input module (104) collects signals from the current sensor (114) and from an unknown data;
at least two signal preprocessing module (106a, 106b) each connected to the unknown data source and the current sensor (114) for improving the quality of signals received, wherein the preprocessing module (106a, 106b) uses welch method for preprocessing the received signals and converges the signals; characterized in that
at least two feature extraction module (108a, 108b) connected to each of the two-signal preprocessing module (106a, 106b) for extracting the preprocessed signals, wherein the feature extraction module (108a, 108b) extracts the features of the preprocessed signals to identify the fault in the electronic device using findpeaks function;
a classification module (110) connected to the feature extraction module (108a, 108b) for classifying the extracted features to detect presence of fault occurring in the electronic device, wherein the classification module (110) uses a K-Nearest neighbor (K-NN) classifier to train the obtained extracted features, wherein when the extracted features are trained, a zero value is returned by the classifier when no fault is detected, else a value one is returned; and
a prediction module (112) connected to the classification module (110) and the feature extraction module (108a, 108b) of the unknown data source for identifying the category of fault using at least three fault indices, wherein a first fault indices is activated when an open circuit fault occurs, wherein a second fault indices is activated when a short circuit fault occurs, wherein a third fault indices is activated when a faulty sensor and healthy electronic device condition.
2. The system as claimed in claim 1, wherein the electronic device is a traction inverter.
3. The system as claimed in claim 1, wherein the three fault indices are represented as F1, F2 and F3, wherein F1 fault indices is activated when an open circuit fault occurs, wherein F2 fault indices is activated when a short circuit fault occurs, wherein F3 fault indices is activated when a faulty sensor and healthy electronic device condition.
4. The system as claimed in claim 1, wherein the fault in the electronic device occurs due to wrong gate voltage, lifting of a bonding conductor present in the electronic device due to thermal cycling, operator malfunction, and switch abruption.
5. A method (200) for diagnosing fault in an electronic device (102), wherein the method (200) comprises of:
obtaining a plurality of signals from the electronic device using a current sensor (114) associated to an input module (104), wherein measuring current flowing in the electronic device using the current sensor (114), wherein collecting signals from the current sensor (114) and from an unknown data using the input module (104);
improving the quality of signals received using at least two signal preprocessing module (106a, 106b) each connected to the unknown data source and the current sensor (114), wherein preprocessing the received signals and converges the signals using welch method of the preprocessing module (106a, 106b); characterized in that extracting the preprocessed signals using at least feature extraction module (108a, 108b) connected to each of the two-signal preprocessing module (106a, 106b), wherein identifying the fault in the electronic device using find peaks function of the feature extraction module (108a, 108b); classifying the extracted features to detect presence of fault occurring in the electronic device using a classification module (110) connected to the feature extraction module (108a, 108b), wherein training the obtained extracted features using the classification module (110) consisting of a K-Nearest neighbor (K-NN) classifier, wherein when the extracted features are trained, a zero value is returned by the classifier when no fault is detected, else a value one is returned; and identifying the category of fault using at least three fault indices using a prediction module (112) connected to the classification module (110) and the feature extraction module (108a, 108b) of the unknown data source, wherein a first fault indices is activated when an open circuit fault occurs, wherein a second fault indices is activated when a short circuit fault occurs, wherein a third fault indices is activated when a faulty sensor and healthy electronic device condition.
FIGURE 2 FIGURE 1
FIGURE 3
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021104319A AU2021104319A4 (en) | 2021-07-20 | 2021-07-20 | A system for traction inverter fault detection and a method thereof |
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| Application Number | Priority Date | Filing Date | Title |
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| AU2021104319A AU2021104319A4 (en) | 2021-07-20 | 2021-07-20 | A system for traction inverter fault detection and a method thereof |
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| AU2021104319A4 true AU2021104319A4 (en) | 2022-04-14 |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116165473A (en) * | 2023-04-26 | 2023-05-26 | 广东工业大学 | Real-time tracing method for network side overcurrent faults of train traction transmission system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116165473A (en) * | 2023-04-26 | 2023-05-26 | 广东工业大学 | Real-time tracing method for network side overcurrent faults of train traction transmission system |
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