scholarly journals CNN-Based Fault Detection for Smart Manufacturing

2021 ◽  
Vol 11 (24) ◽  
pp. 11732
Author(s):  
Dhiraj Neupane ◽  
Yunsu Kim ◽  
Jongwon Seok ◽  
Jungpyo Hong
Keyword(s):  
Fault Detection ◽  
Intelligent Systems ◽  
Sequence Data ◽  
Daily Basis ◽  
Time Sequence ◽  
Fault Detection And Diagnosis ◽  
Training Data ◽  
Smart Manufacturing ◽  
Computationally Intensive ◽  
Intelligent Methods

A smart factory is a highly digitized and networked production facility based on smart manufacturing. A smart manufacturing plant is the result of intelligent systems deployed in the factory. Smart factories have higher production volumes and are prone to machine failures when operating in almost all applications on a daily basis. With the growing concept of smart manufacturing required for Industry 4.0, intelligent methods for detecting and classifying bearing faults have become a subject of scientific research and interest. In this paper, a deep learning-based 1-D convolutional neural network is proposed using the time-sequence bearing data from the Case Western Reserve University (CWRU) bearing database. Four different sets of data are used. The proposed method achieves state-of-the-art accuracy even with a small amount of training data. For the sensitivity analysis of the proposed method, metrics such as precision, recall, and f-measure are determined. Next, we compare the proposed method with a 2-D CNN that uses two-dimensional image illustrations of raw data as input. This method shows the effectiveness of using 1-D CNNs over 2-D CNNs for time-sequence data. The proposed method is computationally inexpensive and outperforms the most complex and computationally intensive algorithms used for bearing fault detection and diagnosis.

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.

Operational Reliability Improvement of Plants Through Intelligent Fault Detection and Diagnosis

2003 ◽  
Author(s):  
E. Ricky Odoom
Keyword(s):  
Fault Detection ◽  
Intelligent Systems ◽  
Operational Reliability ◽  
Fault Detection And Diagnosis ◽  
Complex Dynamic ◽  
Artificial Intelligent ◽  
Reliability Improvement ◽  
Process Plants ◽  
Basic Concepts ◽  
Detection And Diagnosis

Real-time Fault Detection and Diagnosis of modern dynamic process plants are continuously receiving increasing attention both theoretically and practically. In recent years, attempts have been made to apply Artificial Intelligence techniques to the Fault Detection Diagnosis task for improving the operational reliability of complex dynamic plants. The aim of this paper is to discuss the basic concepts, issues and tools of some of the emerging intelligence technologies for Fault Detection and Diagnosis schemes. The emphasis is given to the methods, which are based on Artificial Intelligent systems and which are appropriate for diagnosing faults in complex dynamic plants.

scholarly journals Development and Validation of a Data-Driven Fault Detection and Diagnosis System for Chillers Using Machine Learning Algorithms

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1945
Author(s):  
Icksung Kim ◽  
Woohyun Kim
Keyword(s):  
Fault Detection ◽  
Classification Accuracy ◽  
Energy Savings ◽  
Cost Savings ◽  
Fault Detection And Diagnosis ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Data Driven ◽  
Classification Methods ◽  
Detection And Diagnosis

Fault detection and diagnosis (FDD) systems enable high cost savings and energy savings that could have economic and environmental impact. This study aims to develop and validate a data-driven FDD system for a chiller. The system uses historical operation data to capture quantitative correlations among system variables. This study evaluated the effectiveness and robustness of eight FDD classification methods based on the experimental data of the chiller (the ASHRAE 1043-RP project). The training data used for the FDD system is classified into four cases. Moreover, true and false positive rates are used to characterize the performance of the classification methods. The results show that local fault is not significantly sensitive to training data, and shows high classification accuracy for all cases. The system fault has a significant effect on the amount of data and the severity levels on the classification accuracy.

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 Developing an Approach for Fault Detection and Diagnosis of Angular Velocity Sensors

2021 ◽  
Vol vm02 (is01) ◽  
pp. 15-21
Author(s):  
Dinh-dung Nguyen ◽  
Hong Son Tran ◽  
Thi Thuy Tran ◽  
Dat Dang Quoc ◽  
Hong Tien Nguyen
Keyword(s):  
Fault Diagnosis ◽  
Angular Velocity ◽  
Fault Detection ◽  
Intelligent Systems ◽  
Fault Detection And Diagnosis ◽  
Process Models ◽  
State Observers ◽  
Improvement Method ◽  
Detection And Diagnosis ◽  
Sensor Detection

Angular velocity sensor detection and diagnosis become increasingly essential for the improvement of reliability, safety, and efficiency of the control system on aircraft. The classical methods for fault detection and diagnosis are limit or trend checking of some measurable output variables. Due to they do not give a deeper insight and usually do not allow a fault diagnosis, model-based methods of fault detection and diagnosis were developed by using input and output signals and applying dynamic process models. These approaches are based on parameter estimation, parity equations, or state observers. This paper presents an improvement method to build algorithm fault diagnosis for angular velocity sensors on aircraft. Based on proposed method, results of paper can be used in designed intelligent systems that can automatically fault detection on aircraft.

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.

Vibratory Data Fusion for Gearbox Fault Detection Using Autoassociative Neural Networks

2005 ◽  
Author(s):  
Alfonso Ferna´ndez del Rinco´n ◽  
Pablo Garci´a Ferna´ndez ◽  
Fernando Viadero Rueda ◽  
Ramo´n Sancibria´n Herrera
Keyword(s):  
Fault Detection ◽  
Prediction Error ◽  
Linear Prediction ◽  
Fault Detection And Diagnosis ◽  
Fault Prediction ◽  
Training Data ◽  
Training Set ◽  
Connection Weight ◽  
Data Set ◽  
Gear Fault

This paper deals with gearbox fault detection by vibration analysis. A new processing procedure is proposed which uses the information from several acquisition channels. The approach is based on the assumption that there is a non-linear relationship among the instantaneous vibration magnitude registered for each measurement location. This relationship is captured in the connection weight matrix of an Autoassociative Artificial Neural Network (AANN), which is trained to provide an output vector equal to the input one. In this work, the time synchronous average signal (TSA) for each channel corresponding to the no fault condition is used to train an AANN. Once the AANN is trained, it is used with new data registers as a linear prediction error filter. If the new register contains the same data structure as the training set the prediction error will be low and the machine is working properly. Otherwise, when the new register differs from the training set, as a consequence of a fault, prediction error will be increased in each channel. In this way the information from not only one channel but more than one is used for fault detection and diagnosis as the error signal depends on the TSA signal from all channels. The proposed approach provides a new tool for gear fault detection that is compared on the basis of experimental registers with the most traditional gear processing tools based on TSA such as residual and regular signals. The possibility of generalizing the net prediction capabilities using a training data set that contains several load cases is also explored.

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