scholarly journals A Hierarchical Approach to Improve the Interpretability of Causality Maps for Plant-Wide Fault Identification

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 823
Author(s):  
Natali van Zijl ◽  
Steven Martin Bradshaw ◽  
Lidia Auret ◽  
Tobias Muller Louw
Keyword(s):  
Fault Detection And Diagnosis ◽  
Causality Analysis ◽  
Hierarchical Approach ◽  
Transitive Reduction ◽  
Root Cause ◽  
Processing Plants ◽  
Detection And Diagnosis ◽  
Fault State ◽  
Root Cause Identification

Modern mineral processing plants utilise fault detection and diagnosis to minimise time spent under faulty conditions. However, a fault originating in one plant section (PS) can propagate throughout the entire plant, obscuring its root cause. Causality analysis identifies the cause–effect relationships between process variables and presents them in a causality map to inform root cause identification. This paper presents a novel hierarchical approach for plant-wide causality analysis that decreases the number of nodes in a causality map, improving interpretability and enabling causality analysis as a tool for plant-wide fault diagnosis. Two causality maps are constructed in subsequent stages: first, a dimensionally reduced, plant-wide causality map used to localise the fault to a PS; second, a causality map of the identified PS used to identify the root cause. The hierarchical approach accurately identified the true root cause in a well-understood case study; its plant-wide map consisted of only three nodes compared to 15 nodes in the standard causality map and its transitive reduction. The plant-wide map required less fault-state data, time series in the order of hours or days instead of weeks or months, further motivating its application in rapid root cause analysis.

MULTI–STATE ANALYSIS OF PROCESS STATUS USING MULTILEVEL FLOW MODELLING AND BAYESIAN NETWORK

2016 ◽  
Vol 78 (8-3) ◽  
Author(s):  
Mohamed A. R. Khalil ◽  
Arshad Ahmad ◽  
Tuan Amran Tuan Abdullah ◽  
Ali Al-Shatri ◽  
Ali Al-Shanini
Keyword(s):  
Bayesian Network ◽  
Fault Detection And Diagnosis ◽  
Success Rates ◽  
Probabilistic Information ◽  
Modelling Framework ◽  
Predicting Success ◽  
Detection And Diagnosis ◽  
Probability Information ◽  
Multilevel Flow Modeling

Multilevel Flow Modeling (MFM) model maps functionality of components in a system through logical interconnections and is effective in predicting success rates of tasks undertaken. However, the output of this model is binary, which is taken at its extrema, i.e., success and failure, while in reality, the operational status of plant components often spans between these end. In this paper, a multi-state model is proposed by adding probabilistic information to the modelling framework. Using a heat exchanger pilot plant as a case study, the MFM model is transformed into its fault tree [1] equivalent to incorporate failure probability information. To facilitate computations, the FT model is transformed into Bayesian Network model, and applied for fault detection and diagnosis problems. The results obtained illustrate the effectiveness and feasibility of the proposed method.

scholarly journals Background

2022 ◽  
pp. 1-15
Author(s):  
Jing Wang ◽  
Jinglin Zhou ◽  
Xiaolu Chen
Keyword(s):  
Twentieth Century ◽  
Fault Detection ◽  
Health Monitoring ◽  
Manufacturing Process ◽  
Rapid Development ◽  
Scientific Field ◽  
Fault Detection And Diagnosis ◽  
Root Cause ◽  
Detection And Diagnosis

AbstractFault detection and diagnosis (FDD) technology is a scientific field emerged in the middle of the twentieth century with the rapid development of science and data technology. It manifests itself as the accurate sensing of abnormalities in the manufacturing process, or the health monitoring of equipment, sites, or machinery in a specific operating site. FDD includes abnormality monitoring, abnormal cause identification, and root cause location.

scholarly journals Root Cause Analysis of a Printed Circuit Board (PCB) Failure in a Public Transport Communication System

2022 ◽  
Vol 12 (2) ◽  
pp. 640
Author(s):  
Cher-Ming Tan ◽  
Hsiao-Hi Chen ◽  
Jing-Ping Wu ◽  
Vivek Sangwan ◽  
Kun-Yen Tsai ◽  
...  
Keyword(s):  
Printed Circuit Board ◽  
Failure Modes ◽  
Essential Element ◽  
Circuit Board ◽  
Cause Analysis ◽  
Printed Circuit ◽  
Root Cause ◽  
Burn Out ◽  
Root Cause Identification

A printed circuit board (PCB) is an essential element for practical circuit applications and its failure can inflict large financial costs and even safety concerns, especially if the PCB failure occurs prematurely and unexpectedly. Understanding the failure modes and even the failure mechanisms of a PCB failure are not sufficient to ensure the same failure will not occur again in subsequent operations with different batches of PCBs. The identification of the root cause is crucial to prevent the reoccurrence of the same failure. In this work, a step-by-step approach from customer returned and inventory reproduced boards to the root cause identification is described for an actual industry case where the failure is a PCB burn-out. The failure mechanism is found to be a conductive anodic filament (CAF) even though the PCB is CAF-resistant. The root cause is due to PCB de-penalization. A reliability verification to assure the effectiveness of the corrective action according to the identified root cause is shown to complete the case study. This work shows that a CAF-resistant PCB does not necessarily guarantee no CAF and PCB processes can render its CAF resistance ineffective.

scholarly journals An Autonomic Cycle of Data Analysis Tasks for the Supervision of HVAC Systems of Smart Building

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3103
Author(s):  
Jose Aguilar ◽  
Douglas Ardila ◽  
Andrés Avendaño ◽  
Felipe Macias ◽  
Camila White ◽  
...  
Keyword(s):  
Data Analysis ◽  
Fault Detection ◽  
Hvac Systems ◽  
Economic Benefits ◽  
Fault Detection And Diagnosis ◽  
Hvac System ◽  
Smart Buildings ◽  
Detection And Diagnosis ◽  
Early Fault Detection

Early fault detection and diagnosis in heating, ventilation and air conditioning (HVAC) systems may reduce the damage of equipment, improving the reliability and safety of smart buildings, generating social and economic benefits. Data models for fault detection and diagnosis are increasingly used for extracting knowledge in the supervisory tasks. This article proposes an autonomic cycle of data analysis tasks (ACODAT) for the supervision of the building’s HVAC systems. Data analysis tasks incorporate data mining models for extracting knowledge from the system monitoring, analyzing abnormal situations and automatically identifying and taking corrective actions. This article shows a case study of a real building’s HVAC system, for the supervision with our ACODAT, where the HVAC subsystems have been installed over the years, providing a good example of a heterogeneous facility. The proposed supervisory functionality of the HVAC system is capable of detecting deviations, such as faults or gradual increment of energy consumption in similar working conditions. The case study shows this capability of the supervisory autonomic cycle, usually a key objective for smart buildings.

Root Cause Finding of a Diode Leakage Failure Using Scanning Magnetic Microscopy and ToF-SIMS as Key Methods

2006 ◽  
Author(s):  
H. Preu ◽  
W. Mack ◽  
T. Kilger ◽  
B. Seidl ◽  
J. Walter ◽  
...  
Keyword(s):  
Failure Analysis ◽  
Quantum Interference ◽  
Leakage Currents ◽  
Root Cause ◽  
Microelectronic Devices ◽  
Successful Approach ◽  
Magnetic Microscopy ◽  
Leakage Failure ◽  
Root Cause Identification

Abstract One challenge in failure analysis of microelectronic devices is the localization and root cause finding of leakage currents in passives. In this case study we present a successful approach for failure analysis of a diode leakage failure. An analytical flow will be introduced, which contains standard techniques as well as SQUID (superconducting quantum interference device) scanning magnetic microscopy and ToFSIMS as key methods for localization and root cause identification. [1]

scholarly journals VODCA: Verification of Diagnosis Using CAM-Based Approach for Explainable Process Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6858
Author(s):  
Cheolhwan Oh ◽  
Jongpil Jeong
Keyword(s):  
Neural Network ◽  
Process Monitoring ◽  
Fault Detection And Diagnosis ◽  
System Stability ◽  
Manufacturing Companies ◽  
End User ◽  
Industrial Sites ◽  
Detection And Diagnosis ◽  
Activation Map

Process monitoring at industrial sites contributes to system stability by detecting and diagnosing unexpected changes in a system. Today, as the infrastructure of industrial sites is advancing because of the development of communication technology, vast amounts of data are generated, and the importance of a way to effectively monitor such data in order to diagnose a system is increasing daily. Because a method based on a deep neural network can effectively extract information from a large amount of data, methods have been proposed to monitor processes using such networks to detect system faults and abnormalities. Neural-network-based process monitoring is effective in detecting faults, but has difficulty in diagnosing because of the limitations of the black-box model. Therefore, in this paper we propose a process-monitoring framework that can detect and diagnose faults. The proposed method uses a class activation map that results from diagnosis of faults and abnormalities, and verifies the diagnosis by post-processing the class activation map. This improves the detection of faults and abnormalities and generates a class activation map that provides a more verified diagnosis to the end user. In order to evaluate the performance of the proposed method, we did a simulation using publicly available industrial motor datasets. In addition, after establishing a system that can apply the proposed method to actual manufacturing companies that produce sapphire nozzles, we carried out a case study on whether fault detection and diagnosis were possible.

Sign in / Sign up

Export Citation Format

Share Document