Comparison of a Few Fault Diagnosis Methods on Sparse Variable Length Time Series Sequences

2007 ◽  
Author(s):  
Lokesh Kumar Sambasivan ◽  
Venkataramana Bantwal Kini ◽  
Srikanth Ryali ◽  
Joydeb Mukherjee ◽  
Dinkar Mylaraswamy
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Gas Turbine ◽  
Fault Detection And Diagnosis ◽  
Variable Length ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Detection And Diagnosis ◽  
Time Sequences

Accurate gas turbine engine Fault Detection and Diagnosis (FDD) is essential to improving aircraft safety as well as in reducing airline costs associated with delays and cancellations. This paper compares broadly three methods of fault detection and diagnosis (FDD) dealing with variable length time sequences. Chosen methods are based on Dynamic Time Warping (DTW), k-Nearest Neighbor method, Hidden Markov Model (HMM) and a Support Vector Machine (SVM) which makes use of DTW ingeniously as its kernel. The time sequences are obtained from Turbo Propulsion Engines in their nominal conditions and two faulty conditions. Typically there is paucity of faulty exemplars and the challenge is to come up with algorithms which work reasonably well under such circumstances. Also, normalization of data plays a significant role in determining the performance of the classifiers used for FDD in terms of their detection rate and false positives. In particular spherical normalization has been explored considering the advantage of its superior normalization properties. Given sparse training data how well each of these algorithms performs is shown by means of tests performed on time series data collected at normal and faulty modes from a turbofan gas turbine propulsion engine and the results are presented.

scholarly journals Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8163
Author(s):  
Wunna Tun ◽  
Johnny Kwok-Wai Wong ◽  
Sai-Ho Ling
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Fault Detection ◽  
Early Stage ◽  
Fault Detection And Diagnosis ◽  
Training Data ◽  
Support Vector ◽  
Hvac System ◽  
Machine Modeling ◽  
Detection And Diagnosis

The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors used in HVAC system, the faults can be very difficult to detect in the early stage. While numerous fault detection and diagnosis (FDD) methods with the use of statistical modeling and machine learning have revealed prominent results in recent years, early detection remains a challenging task since many current approaches are unfeasible for diagnosing some HVAC faults and have accuracy performance issues. In view of this, this study presents a novel hybrid FDD approach by combining random forest (RF) and support vector machine (SVM) classifiers for the application of FDD for the HVAC system. Experimental results demonstrate that our proposed hybrid random forest–support vector machine (HRF–SVM) outperforms other methods with higher prediction accuracy (98%), despite that the fault symptoms were insignificant. Furthermore, the proposed framework can reduce the significant number of sensors required and work well with the small number of faulty training data samples available in real-world applications.

scholarly journals Fault detection and diagnosis in building energy systems: A tool chain for the automated generation of training data

2021 ◽  
Vol 2042 (1) ◽  
pp. 012083
Author(s):  
Christine van Stiphoudt ◽  
Florian Stinner ◽  
Gerrit Bode ◽  
Alexander Kümpel ◽  
Dirk Müller
Keyword(s):  
Fault Detection ◽  
Energy Systems ◽  
Building Energy ◽  
Fault Detection And Diagnosis ◽  
Training Data ◽  
Real Component ◽  
Energy Management Systems ◽  
Building Energy Systems ◽  
Code Extension ◽  
Detection And Diagnosis

Abstract The application of fault detection and diagnosis (FDD) algorithms in building energy management systems (BEMS) has great potential to increase the efficiency of building energy systems (BES). The usage of supervised learning algorithms requires time series depicting both nominal and component faulty behaviour for their training. In this paper, we introduce a method that automates Modelica code extension of BES models in Python with fault models to approximate real component faults. The application shows two orders of magnitude faster implementation compared to manual modelling, while no errors occur in the connections between fault and component models.

scholarly journals Analisis Pola Prediksi Data Time Series menggunakan Support Vector Regression, Multilayer Perceptron, dan Regresi Linear Sederhana

2019 ◽  
Vol 3 (2) ◽  
pp. 282-287
Author(s):  
Ika Oktavianti ◽  
Ermatita Ermatita ◽  
Dian Palupi Rini
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Multilayer Perceptron ◽  
Time Series Data ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Support Vector ◽  
Contributing Factors ◽  
Testing Data

Licensing services is one of the forms of public services that important in supporting increased investment in Indonesia and is currently carried out by the Investment and Licensing Services Department. The problems that occur in general are the length of time to process licenses and one of the contributing factors is the limited number of licensing officers. Licensing data is a time series data which have monthly observation. The Artificial Neural Network (ANN) and Support Vector Machine (SVR) is used as machine learning techniques to predict licensing pattern based on time series data. Of the data used dataset 1 and dataset 2, the sharing of training data and testing data is equal to 70% and 30% with consideration that training data must be more than testing data. The result of the study showed for Dataset 1, the ANN-Multilayer Perceptron have a better performance than Support Vector Regression (SVR) with MSE, MAE and RMSE values is 251.09, 11.45, and 15.84. Then for dataset 2, SVR-Linear has better performance than MLP with values of MSE, MAE and RMSE of 1839.93, 32.80, and 42.89. The dataset used to predict the number of permissions is dataset 2. The study also used the Simple Linear Regression (SLR) method to see the causal relationship between the number of licenses issued and licensing service officers. The result is that the relationship between the number of licenses issued and the number of service officers is less significant because there are other factors that affect the number of licenses.  

Sign in / Sign up

Export Citation Format

Share Document