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I. Problem Overview II. Methodologies IV. Recommendations V. Conclusion VI. Future Directions III. Objectives IE3100R Systems Design Project | Department of Industrial & Systems Engineering NUS Supervisors: Prof Tan Chin Hon | Prof He Shuangchi | Prof Bok Shung Hwee Industrial Supervisors: Mr David So | Mr Kevin Chen | Mr Joel Pow Group Members: Gao Yu | Liu Mengyi | Low Ching Nam Raymond | Wang Jiexuan • Poorly formatted and insufficient data: • Design a suitable data format to support quantitative analysis of data Inadequate database management: • Develop an algorithm to merge and centralize the different databases • Design a new data collection and storage process to improve overall quality of data Inefficent troubleshooting process: • Design a troubleshooting decision-support system • Increase engineers’ efficiency and accuracy when generating a troubleshooting recommendation Inadequate Database Management Inefficient Trouble-shooting Process • Historical fleet data is not effectively utilized in plane diagnostic reasoning process undermining troubleshooting capability. • Troubleshooting process is time-consuming. Prolong diagnostic analysis might result in plane delay and escalating costs for airlines. Poorly Formatted and Insufficient Data • The presence of abbreviated terms and technical jargons limits quantitative analysis that can be performed on the current data • Maintenance entries are entirely handwritten affecting the accuracy of the data entered into database • Omission of critical data caused by limited writing space available in the current technical logs • Important data needed for troubleshooting decisions is stored in different databases, between which there are no direct linking fields • Entries of data into one database can’t be auto-updated in another databse whose data fields should contain the same information • Looks at a process as a system which consists of interconnected parts • These interconnected parts and their interactions create the characteristics of the whole system • Instead of treating each part as individual entities, a holistic overview of how these parts come together and interact is adopted • Database management mainly deals with managing large data sets and performing operations on the data as requested by its users • When faced with a decentralised data base, schema matching is a useful tool to link the different databases together and facilitate efficient data analysis • Decision analysis is the discipline uses various procedures, methods and tools for identifying, clearly representing and formally assessing the important aspects of a decision • A recommended course of action is then prescribed toachieve the maximum expected utility • Bayesian Network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph • It is used to detect and locate faulty components in the system and contributes to the possibility of ranking possible failures. Systems Thinking Database Management Decision Analysis Bayesian Network • Digitalized maintenance entry to centralize data storage system to facilitate future data analysis. • Intuitive flow of data fields sequence reduces data collection time for engineers. • Prevent omission of key data by creating compulsory data fields for engineers to fill up. Electronic Techlog • Expedite the diagnostic reasoning process with historical fleet data and on board troubleshooting observations. • Highly scalable as this model prototype can be generalized to all sub-systems in a plane. • Robustness of this model will enhance over time when data quality improves with a more centralized data storage system. Bayesian Network Model Database Management • Schema matching of databases is a critical step to enable data analysis, without which the historical fleet data could not be utilised to support effective troubleshooting. • A centralised database will be time-saving and labour-saving, bypassing the steps of matching up the data during each troubleshooting action. Electronic Techlog Features Bayesian Network • Entry Filed: to allow easy differentiation between main- tenance entries and pilot entries. • Fault Messages: to capture the fault message displayed on the aircrafts’ on-board computers. • Integration of the Troubleshooting Manual (TSM) into E-Log: -to start troubleshooting process by inputting TSM refer ence number or fault message -depending on the TSM task initialized, the sequence of steps displayed will follow the sequence in TSM -depending on user input, the engineer will be directed to the next appropriate step in the TSM task • Integration of the Aircraft Maintenance Manual (AMM) into E-Log: easier AMM task reference. • Additional Observation field: to capture any observa- tions made during the troubleshooting process. • Tech Logs: A database containing troubleshooting actions taken for a technical instance. • AIRMAN: A database containing system-generated fault messages during a technical instance. • Flight Logs: A database containing the journey number unique to each journey , journey code and flight date infor- mation ; Introduced for the use of matching up AIRMAN and Tech Logs entries. • Dependency Identification: Fields containing same information type are identified, and are noted down as important identifiers to match the entries up for a particular instance. • UI: A simple button using Microsoft Userform. • Logic: Matching logic is implemented using VBA. Main logic includes normalizing both Airman and Techlog data- base, creating unique identifier for each entry and matching entries with same identifier up. • Techlog Database: from which rectifying actions are extracted. • Airman Database: from which system fault messages are extracted. Based on current decentralized database, integrating will be needed to salvage historical data. The general logic is shown as below. With the introduction of electronic technical log, there is no need to match up the data, as both rectifying action and fault message would be stored in the same entry for each instance. Data analysis on the data is made possible as now all rele- vant data are centralized and linked Database Dependency Identification Architecture for Schema Matching Database Management • Bayesian Network Prototype: to utilize historical fleet data to mini- mize troubleshooting response time by quantifying removal action which yields the highest utility/satisfaction level. • Diagnostic Inference: to investigate the most likely cause of prob- lem in an aircraft given certain fault messages observed. • 2 Layers of Representation of Knowledge: -At the qualitative level, the graphical structure of the network represents the probabilistic dependence or relevance be tween the variables -At the quantitative, level, the conditional probabilities at each node represent the local ‘strengths’ of the dependence rela tionships. Poorly Formatted and Insufficient Data • Ground engineers inputs data of an incident via Elec- tronic Techlog themselves, effectively reducing human error made by data entry per- sonnel who are unfamiliar with professional jargons. • Integration of TSM and AMM into E-log provides better linkage between differ- ent databases and makes it much easier to locate infor- mation. • Inclusion of new data fields facilitates data analysis in trouble-shooting process. Inefficient Trouble-shooting Process • Engineers construct a Bayesian Network Model Prototype based on possible causes of an incident, possible plane fault messages and all possible removal actions, obtained from historical fleet data so that histori- cal records are effectively utilized • Engineers then apply the model in plane diagnostic process by inputting troulbleshooting obser- vations into the model • The model will display a list of predicted value for each compo- nent removal action and engi- neers will make recommenda- tions based on the results gener- ated. This reduces response time for incident troubleshooting. Inadequate Database Management • Extraction of string-formatted data enbaled by VBA Excel allows engineers to make use of data which originally can’t be made use of • Matches and centralizes data across different data- bases is realized through Schema Matching in VBA Excel codes, which effec- tively reduces redundant information engineers need to browse through to locate the relevant data. Based on historial data stored in centralized database, engineers carry out data analysis to estimate the probabilities needed to construct the Bayes- ian Network Prototype. The node is the network is as follows: • Reference Node: various problem causes and fault messages. • Decision Node: a set of component removal decisions made by engi- neer. • Utility Node: a.k.a ‘SatisfactionLevel’, which stored a set of satisfaction scored given different combinations of fault messages observed and removal actions. For example, when only ‘F/CTL FLAP SYS 1 FAULT’ fault message is ob- served and for different removal action, the level of satisfaction obtained by engineer are different and depends on whether the same fault happens again. In Bayesian network, the presence of relevance arc indicate possi- ble relevance between nodes while absence of arc represent definite non-relevance, since the team can’t assume definite non-relevance be- tween differnet nodes, all causes of problem are connected to fault mes- sage. Enhancement of Troubleshooting Process via Database Management and Bayesian Network Scaling of Bayesian Network Prototype • Current Bayesian Network is constructed based on one small section in the Trouble Shooting Manual as a proof of concept. If this model is proven to be practical, SIAEC can hire external company to establish a complete model based on complete TSM to facilitate plane trouble diagnostic process. Further Analysis of Historical Data • Current extraction and integration of string-formmated data from various databases is based on limited flight records. A more comprehensive databases will be built in the future to make full use of historical data and provide better probabilistic estimation. Self Learning Database • New incident record will be integrated with historical data via the central- ized data management system to account for better statitical accuracy as more incidents take place. Fault Messages Possible Causes Component Removed Satisfaction Level
