Quality Alchemist (品質煉金術師): CAiRS webinar - System Reliability and Maintenance

2021年1月15日星期五

CAiRS webinar - System Reliability and Maintenance - Key Success Factors for your business

The webinar named “System Reliability and Maintenance - Key Success Factors for your business” was organized by Centre for Advances in Reliability and Safety (CAiRS) and supported by HKSTP on 15^th Jan 2021. Local and overseas professional experts and renowned industrialist were invited to share their industrial and research experiences on Reliability Prediction, Prognostics and Health Management, and System Maintenance and Management etc.

Dr. Diganta Das (CALCE) was the first speaker and his topic entitled “Is Your Reliability Prediction Reliable?” Firstly, Dr. Das introduced Reliability and Predicted Reliability. Reliability is the probability that an item will perform its intended function for a specified interval under stated conditions. Predicted Reliability is for the stated conditions of use, and taking into account the design of an item, the reliability computed from the observed, assessed, or extrapolated reliabilities of its parts, interconnects and interfaces.

Then Dr. Das explained the reasons to perform reliability prediction that could understand the risks and take different actions. However, such prediction usually only consider as a contractual requirement or regulatory obligation. He also briefed different method for prediction including reliability prediction handbooks, reliability testing, use of field data and physics of failure, etc.

After that he briefed a timeline for MIL-HDBK-217 since 1940s. And then he described the change of transistors number and features from 1970s to 2010s. MIL-HDBK-217F, Notice 2 was issued in 1995.

However, most prediction form various handbook and different methodologies had large deviation. Problems included incorrectly assume constant failure rates, no information on potential failure modes, mechanisms and sites. Finally, Dr. Diganta Das introduced IEEE Standards 1413 and 1413.1 which provides a framework for reliability prediction of hardware that is a tools for evaluation of reliability predictions.

Dr. Michael H. Azarian (CALCE) was the second speaker and his presentation named “Prognostics and Health Management enabled Maintenance”. Firstly, he introduced health progress timeline from “Start of product life cycle” to “Early incipient fault” to “Component or sub-system failure” and to “System failure”.

And then Dr. Azarian introduced four maintenance strategies and they were “Corrective Maintenance”, “Scheduled Maintenance”, “Condition-based Maintenance” and “Predictive Maintenance”. Then he explained it using automotive health monitoring sample.

After that he briefed Prognostics and Health Management (PHM) that permits the evaluation of system’s reliability in its actual life-cycle conditions. Then PHM process cycle was mentioned. He raised different possible PHM application levels and they were Level 0 – die, Level 1 – component, Level 2 – assembly, Level 3 – module, Level 4 – system, and Level 5 – system of systems.

Finally, Dr. Azarian discussed two Prognostic Approaches that were Physics-of-Failure (PoF) Approach and Data-Driven Approach. The PoF-Based PHM methodology was showed in the following diagram.

Data Driven PHM was discussed. Machine learning based on statistical methods is well-suited for PHM because it is capable of actively learning about the system and its dynamics, faults, and failures. Lastly, Dr. Azarian concluded that both approaches are used to evaluate the health of a system, to predict its remaining useful life, and to implement risk-mitigating actions such as preventative maintenance.

The third speaker was Ir. Wilson Kwok (Head of Technical Services, The Hongkong Electric Co., Ltd.) and his presentation topic was “Application of AI in the Reliability and Security of Power Grids”. In the beginning, Ir. Kwok said the meaning of smart cities is to use of connected technologies to improve efficiency and quality of life in a sustainable way.

Then Ir. Kwok compared smart cities and human body that both key elements included brain (decision making), senses (CCTV) and body (ICT). He also shared some application such as hot spot on apparatus, water dripping and flooding.

And then he introduced their Intelligent Volt-VAR Regulation (IVAR) System Architecture and Software Design

After that he briefed the history of medium voltage (MV) cable diagnostic assessment since 1980.

At the end, Ir. Wilson Kwok introduced that they adopt AI methodology to process and analyze data and review the causes of faults for cable life assessment.

The last speaker was Dr Siqi Bu (Associate Professor, PolyU) and his title named “Data-Driven Techniques in enhancing Reliability and Security of Power Networks”. Firstly Dr. Bu briefed the background of power industry transformation as 4D including Decarbonization, Digitization, Decentralization and Deregulation.

Then Dr. Bu briefed the predictive maintenance of power equipment. The future predictive maintenance (PdM) would be AI-based, Data-driven approach.

After that Dr. Bu mentioned tools of Situation Awareness (SA) that modern power systems were being treated as comprehensive cyber physical systems (CPS). He proposed an Enhanced Situation Awareness Based on Random Matrix and Deep Learning.

Finally, Dr. Siqi Bu discussed CNN-LSTM model to achieve the collaboratively spatiotemporal data mining. Technically, CNN module is to extract and learn spatially correlated features and LSTM module is to handle temporal correlation of the extracted feature. Lastly he concluded that the very fast post-fault actions (PFAs) could be implemented by the system operators to avoid any unstable operational status of the electric power system.

Q&A Session

At the end, Prof. Winco Yung (Centre Director, CAiRS) gave closing remark. He said we needed data and knowledge domains from different industries for employing AI in reliability modeling to enhance safety level and CAiRS had different experts and partners to achieve this goal.