scholarly journals Fault Log Recovery Using an Incomplete-data-trained FDA Classifier for Failure Diagnosis of Engineered Systems

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Hyunjae Kim ◽  
Jong Moon Ha ◽  
Jungho Park ◽  
Sunuwe Kim ◽  
Keunsu Kim ◽  
...  
Keyword(s):  
Incomplete Data ◽  
Failure Diagnosis ◽  
Fault Classification ◽  
Industrial Plant ◽  
Data Sets ◽  
Fisher Discriminant Analysis ◽  
Recovery Method ◽  
Fisher Discriminant ◽  
Engineered Systems ◽  
The Given

In the 2015 PHM Data Challenge Competition, the goal of the competition problem was to diagnose failure of industrial plant systems using incomplete data. The available data consisted of sensor measurements, control reference signals, and fault logs. A detailed description of the plant system of interest was not revealed, and partial fault logs were eliminated from the dataset. This paper presents a fault log recovery method using a machine-learning-based fault classification approach for failure diagnosis. For optimal performance, it was critical to be able to utilize a set of incomplete data and to select relevant features. First, physical interpretation of the given data was performed to select proper features for a fault classifier. Second, Fisher discriminant analysis (FDA) was employed to minimize the effect of outliers in the incomplete data sets. Finally, the type of the missing fault logs and the duration of the corresponding faults were recovered. The proposed approach, based on the use of an incomplete-data-trained FDA classifier, led to the second-highest score in the 2015 PHM Data Challenge Competition.

Classification of Complex Machine Data to be Used for Structural Health Monitoring Purposes

2014 ◽  
Author(s):  
S. Schiffer ◽  
D. Söffker
Keyword(s):  
Measurement Data ◽  
Training Data ◽  
Fault Classification ◽  
Industrial Processes ◽  
Data Sets ◽  
Statistical Features ◽  
Model Classification ◽  
Fuzzy Like ◽  
The Given

In this contribution a recently developed new modeling and classification approach to be used with streamed measurement data of industrial processes is applied. This briefly repeated approach can be used for fault classification and diagnostic purposes. The approach is based on a fuzzy-like modeling using statistical features from training data. Based on the trained model classification can be realized allowing to distinguish unknown data sets to the given number of data classes each related to states. Beside the brief introduction to the proposed approach, experimental data are used to demonstrate the approach and the complex example distinguishing different wear states of machine components during operation.

scholarly journals Kernel Fisher Discriminant Analysis Based on a Regularized Method for Multiclassification and Application in Lithological Identification

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Dejiang Luo ◽  
Aijiang Liu
Keyword(s):  
Discriminant Analysis ◽  
Characteristic Equation ◽  
Well Logs ◽  
Data Sets ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis ◽  
Lithology Identification ◽  
Optimal Kernel ◽  
Regularized Method

This study aimed to construct a kernel Fisher discriminant analysis (KFDA) method from well logs for lithology identification purposes. KFDA, via the use of a kernel trick, greatly improves the multiclassification accuracy compared with Fisher discriminant analysis (FDA). The optimal kernel Fisher projection of KFDA can be expressed as a generalized characteristic equation. However, it is difficult to solve the characteristic equation; therefore, a regularized method is used for it. In the absence of a method to determine the value of the regularized parameter, it is often determined based on expert human experience or is specified by tests. In this paper, it is proposed to use an improved KFDA (IKFDA) to obtain the optimal regularized parameter by means of a numerical method. The approach exploits the optimal regularized parameter selection ability of KFDA to obtain improved classification results. The method is simple and not computationally complex. The IKFDA was applied to theIrisdata sets for training and testing purposes and subsequently to lithology data sets. The experimental results illustrated that it is possible to successfully separate data that is nonlinearly separable, thereby confirming that the method is effective.

scholarly journals Revisiting the performance of PCA versus FDA versus Simple Projection for image recognition

2021 ◽  
Vol 8 (1) ◽  
pp. 085-095
Author(s):  
Fahad Bin Mostafa ◽  
Md Sakhawat Hossain ◽  
Md Easin Hasan
Keyword(s):  
Discriminant Analysis ◽  
Reduction Process ◽  
Image Data ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Fisher Discriminant Analysis ◽  
Linear Discriminant ◽  
Fisher Discriminant ◽  
Value Decomposition

In this paper, our main aim is to show a better dimension reduction process of high dimensional image data sets from several existing techniques. To verify it we start with most useful singular value decomposition to reduce the dimensionality of data to incorporate principal components. On the other hand, we classify data in advance to work out Fisher’s discriminant. From many real-world examples, we set a very well-known paradigm of analysis using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) or Fisher Discriminant Analysis (FDA) and Simple Projection (SP) to recognize people from their facial images. We consider that we have some images of known people that can be used to compare and recognize new images (of the same set of face images). Moreover, we show graphical and tabular representation for average performance of correct recognition as well as analyze the effectiveness of three different machine learning techniques.

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