Using Weather and Schedule based Pattern Matching and Feature based PCA for Whole Building Fault Detection — Part II Field Evaluation

2021 ◽  
pp. 1-16
Author(s):  
Yimin Chen ◽  
Jin Wen ◽  
L. James Lo
Keyword(s):  
Fault Detection ◽  
Pattern Matching ◽  
Detection Method ◽  
Principal Component ◽  
Field Evaluation ◽  
Interval Data ◽  
Automation System ◽  
Hvac System ◽  
High Detection Rate ◽  
Feature Based

Abstract In a heating, ventilation and air conditioning (HVAC) system, a whole building fault (WBF) refers to a fault that occurs in one component but may trigger additional faults/abnormalities on different components or subsystems resulting in impacts on the energy consumption or indoor air quality in buildings. At the whole building level, interval data collected from various components/subsystems can be employed to detect WBFs. In the Part I of this study, a novel data-driven method which includes weather and schedule-based Pattern Matching (WPM) procedure and a feature based principal component analysis PCA (FPCA) procedure was developed to detect the WBF. This article is the second of a two-part study of the development of the whole building fault detection method. In the Part II of the study (this paper), various WBFs were designed and imposed in the HVAC system of a campus building. Data from both imposed fault and naturally-occurred faults were collected through the Building Automation System to evaluate the developed fault detection method. Evaluation results show that the developed WPM-FPCA method reaches a high detection rate and a low false alarm rate.

Using Weather and Schedule based Pattern Matching and Feature based PCA for Whole Building Fault Detection — Part I Development of the Method

2021 ◽  
pp. 1-23
Author(s):  
Yimin Chen ◽  
Jin Wen ◽  
L. James Lo
Keyword(s):  
Fault Detection ◽  
Pattern Matching ◽  
Selection Process ◽  
Principal Component ◽  
Component Level ◽  
Operation Performance ◽  
Modeling Process ◽  
Baseline Status ◽  
Feature Based ◽  
Using Data

Abstract A whole building fault (WBF) refers to a fault occurring in one component, but may cause impacts on other components or subsystems, or arise impacts of significant energy consumption and thermal comfort. Conventional methods which targeted at the component level fault detection cannot be successfully employed to detect a WBF because of the fault propagation among the closely coupled equipment or subsystems. Therefore, a novel data-driven method named weather and schedule-based pattern matching (WPM) and feature based principal component analysis (FPCA) method for WBF detection is developed. Three processes are established in the WPM-FPCA method to address three main issues in the WBF detection. First, a feature selection process is used to pre-select data measurements which represent a whole building's operation performance under a satisfied status, namely baseline status. Secondly, a WPM process is employed to locate weather and schedule patterns in the historical baseline database, that are similar to that from the current/incoming operation data, and to generate a WPM baseline. Lastly, PCA models are generated for both the WPM baseline data and the current operation data. Statistic thresholds used to differentiate normal and abnormal (faulty) operations are automatically generated in this PCA modeling process. The PCA models and thresholds are used to detect WBF. This paper is the first of a two-part study. Performance evaluation of the developed method is conducted using data collected from a real campus building and will be described in the second part of this paper.

scholarly journals Development and Field Evaluation of Data-driven Whole Building Fault Detection and Diagnosis Strategy

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Yimin Chen ◽  
Jin Wen
Keyword(s):  
Fault Detection ◽  
Principal Component ◽  
Field Evaluation ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Component Level ◽  
Automation Systems ◽  
Tightly Coupled ◽  
Detection And Diagnosis

Faults, i.e., malfunctioned sensors, components, control, and systems, in a building have significantly adverse impacts on the building’s energy consumption and indoor environment. To date, extensive research has been conducted on the development of component level fault detection and diagnosis (FDD) for building systems, especially the Heating, Ventilating, and Air Conditioning (HVAC) system. However, for faults that have multi-system impacts, component level FDD tools may encounter high false alarm rate due to the fact that HVAC subsystems are often tightly coupled together. Hence, the detection and diagnosis of whole building faults is the focus of this study. Here, a whole building fault refers to a fault that occurs in one subsystem but triggers abnormalities in other subsystems and have significant adverse whole building energy impact. The wide adoption of building automation systems (BAS) and the development of machine learning techniques make it possible and cost-efficient to detect and diagnose whole building faults using data-driven methods. In this study, a whole building FDD strategy which adopts weather and schedule information based pattern matching (WPM) method and feature based Principal Component Analysis (FPCA) for fault detection, as well as Bayesian Networks (BNs) based method for fault diagnosis is developed. Fault tests are implemented in a real campus building. The collected data are used to evaluate the performance of the proposed whole building FDD strategies.

A Principal Component Analysis Based Fault Detection Method in Etch Process of Semiconductor Manufacturing

2012 ◽  
Vol 522 ◽  
pp. 793-798 ◽  
Author(s):  
Jun Gang Yang ◽  
Jie Zhang ◽  
Jian Xiong Yang ◽  
Ying Huang
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Semiconductor Manufacturing ◽  
Detection Method ◽  
Main Idea ◽  
Simple Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Training Data ◽  
Detection Model

A Principal Component Analysis based Fault Detection method is proposed here to detect faults in etch process of semiconductor manufacturing. The main idea of this method is to calculate the loading vector and build the fault detection model according to training data. Using this model, the main information of fault data can be obtained immediately and easily. Also the principal component subspace and residual subspace can be constructed. Then, faults are detected by calculating Squared Prediction Error. Finally, an industrial example of Lam 9600 TCP metal etcher at Texas Instruments is used to demonstrate the performance of the proposed PCA-based method in fault detection, and the results show that it has such advantages as simple algorithm and low time cost, thus especially adapts to the real time fault detection of semiconductor manufacturing.

Fault detection method based on principal component difference associated with DPCA

2018 ◽  
Vol 33 (1) ◽  
pp. e3082 ◽  
Author(s):  
Cheng Zhang ◽  
Qingxiu Guo ◽  
Yuan Li
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Principal Component
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