Decision Support for Remote Monitoring and Diagnostics of Aircraft Engine Using Influence Diagrams

2007 ◽  
Author(s):  
Mark Osborn ◽  
LiJie Yu
Keyword(s):  
Remote Monitoring ◽  
Service Providers ◽  
Aircraft Engine ◽  
Influence Diagrams ◽  
Large Set ◽  
Quality Indices ◽  
Secondary Damage ◽  
Desirable Outcome ◽  
Parametric Data ◽  
Monitoring And Diagnostics

FAA regulations require the monitoring of all commercial aircraft engines to ensure airworthiness. In doing so, it provides economic advantages to engine owners to monitor engine performance and resolve identified issues in a timely manner to reduce operational costs or avoid secondary damage. Various remote monitoring and diagnostics service providers exist in the marketplace. However, a common understanding among most of them is that given limited time and information, it is an extremely difficult task to make quick and optimized decisions. Difficulties arise from the fact that an aircraft engine is a complex system and demands considerable expertise to diagnose, but also due to the uncertainty in estimating an engine’s true physical state because of measurement and process noise. Therefore, it is often difficult to decide what action to take in order to achieve the most desirable outcome. In this paper, a cost sensitive engine diagnostic and decision making methodology is described. Diagnostic tool performance at various decision thresholds is estimated over a large set of validated historical cases to evaluate sensitivity, specificity and other quality indices. These quality indices and a set of cost functions are utilized in influence diagrams to derive the optimized decision model in order to minimize costs given the uncertain engine condition and noisy parametric data.

A Data Fusion Approach for Aircraft Engine Fault Diagnostics

2007 ◽  
Author(s):  
Xiao Hu ◽  
Neil Eklund ◽  
Kai Goebel
Keyword(s):  
Fault Diagnosis ◽  
Aircraft Engine ◽  
The State ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Commercial Aircraft ◽  
Secondary Damage ◽  
Detection And Identification ◽  
Parametric Data ◽  
Diagnostic Models

Accurate and timely detection and identification of aircraft engine faults is critical to keeping the engine and aircraft in a healthy operating state. Early detection of faults increases the window of opportunity to schedule maintenance actions both at a convenient time and before the fault progresses and causes equipment downtime and secondary damage to the system. Typically, diagnostic models are built using parametric sensor data to infer the state of the system. However, recording and collecting this data is costly, and it is generally limited to a few snapshots over the course of a flight for commercial aircraft. Another way to recognize faults is through the use of built-in tests that produce error log messages. These tests produce data that is less information rich, but provide insight over the course of the entire flight. Each data source provides a different perspective of the state of the system. Therefore, it may be advantageous to combine information from parametric and nonparametric sources to improve fault diagnosis in terms of accuracy and timeliness of diagnosis. In this paper, we investigate integrating parametric sensor data and nonparametric information in fault diagnosis, specifically the way to parameterize nonparametric information for use in diagnostic models that accept only parametric data (e.g., most machine learning techniques). Results from high bypass commercial engines are presented.

Quality, satisfaction and loyalty indices

2018 ◽  
Vol 11 (4) ◽  
pp. 428-446 ◽  
Author(s):  
Ana Ma Castillo Canalejo ◽  
Juan Antonio Jimber del Río
Keyword(s):  
Structural Equation ◽  
Quality Index ◽  
Service Providers ◽  
Perceived Value ◽  
Perceived Quality ◽  
Large Set ◽  
Unique Contribution ◽  
Content Type ◽  
Tourism Services

Purpose The main purpose of this research was to develop a universal model to evaluate the perceived value of tourism services and satisfaction with, and loyalty to, destinations from the consumers’ perspective and demonstrated the model’s applicability in this context. Design/methodology/approach Using the structural equation model, cause and effect relationships were identified between the proposed model’s constructs, and indices of quality, satisfaction and loyalty among tourists were estimated. This system was applied to a large set of data collected with a structured questionnaire distributed to tourists visiting the city of Seville through a non-probabilistic sampling by intentional quotas method. In total, 922 valid surveys were obtained. Findings The indices show that tourists who visit Seville report a high level of loyalty to, and satisfaction with, this place because of the perceived quality of a variety of services. It is observed that the perceived quality index is much higher (17.95 per cent) than the expected quality index, so the quality of the service received by the tourist during his/her visit to Seville is described as excellent. Research limitations/implications Regarding this study’s limitations, other variables could have been included that influence tourist satisfaction, such as the climate, the effect of advertising medium, the prices and the emotional components. In addition, surveying tourists’ expectations before their visit is virtually impossible, as is surveying the same tourists again about their perceived value and satisfaction after their visit. Future lines of research could focus on the intersection of information between tourism offer and demand, providing information about an appropriate balance in specific markets. The proposed model can also be applied to other tourism places that are similar to Seville’s tourism offer, allowing useful comparisons and identification of critical points and ways to improve customer satisfaction continuously. Practical implications By establishing indices of expected and perceived quality and satisfaction and loyalty among tourists, tourism authorities and different economic agents involved in this sector can receive objective information about the results and quality of tourism services. Tourism managers, thus, can set objectives for improvements and competitiveness, as well as building and maintaining customer loyalty. At the same time, these indices allow comparisons with other organisations and places. By facilitating greater transparency in the measurement of quality and satisfaction, service providers connected to tourism can create a platform on which to articulate clearly their contributions to interested parties and local communities. Social implications These results constitute strategies and findings that any tourism place has to consider in the planning and development of its products. Therefore the model can help to encourage a long-term market perspective among tourism sector regulators, investors and agencies. With the information obtained with this model, areas needing improvement can be identified and the appropriate procedures can be put into practice to improve the tourism offer, adjusting it to meet travellers’ needs according to their motivations to travel to the destination. Residents also can benefit from these measures, as their quality of life will improve through upgrades of the city’s tourism facilities. Originality/value The unique contribution of the present study lies in how the indices or indicators of quality of, satisfaction with and loyalty to destinations among tourists are easily measured by applying structural equation modelling. A new approach to measure satisfaction, loyalty and quality is used based on a scale from 0 to 100, and the index results are very useful for comparing different tourist places.

scholarly journals Aircraft Engine Gas-Path Monitoring and Diagnostics Framework Based on a Hybrid Fault Recognition Approach

Aerospace ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 232
Author(s):  
Juan Luis Pérez-Ruiz ◽  
Yu Tang ◽  
Igor Loboda
Keyword(s):  
Aircraft Engine ◽  
Fault Classification ◽  
Test Case ◽  
Diagnostic Systems ◽  
Blind Test ◽  
Self Evaluation ◽  
Promising Tool ◽  
Fault Recognition ◽  
Sparse Representation Classification ◽  
Monitoring And Diagnostics

Considering the importance of continually improving the algorithms in aircraft engine diagnostic systems, the present paper proposes and benchmarks a gas-path monitoring and diagnostics framework through the Propulsion Diagnostic Methodology Evaluation Strategy (ProDiMES) software developed by NASA. The algorithm uses fleet-average and individual engine baseline models to compute feature vectors that form a fault classification with healthy and faulty engine classes. Using this classification, a hybrid fault-recognition technique based on regularized extreme learning machines and sparse representation classification was trained and validated to perform both fault detection and fault identification as a common process. The performance of the system was analyzed along with the results of other diagnostic frameworks through four stages of comparison based on different conditions, such as operating regimes, testing data, and metrics (detection, classification, and detection latency). The first three stages were devoted to the independent algorithm development and self-evaluation, while the final stage was related to a blind test case evaluated by NASA. The comparative analysis at all stages shows that the proposed algorithm outperforms all other diagnostic solutions published so far. Considering the advantages and the results obtained, the framework is a promising tool for aircraft engine monitoring and diagnostic systems.

High Performance Condition Monitoring of Aircraft Engines

2005 ◽  
Author(s):  
James P. Herzog ◽  
Jason Hanlin ◽  
Stephan W. Wegerich ◽  
Alan D. Wilks
Keyword(s):  
Real Time ◽  
Condition Monitoring ◽  
High Performance ◽  
Cost Savings ◽  
Aircraft Engine ◽  
Nonparametric Model ◽  
Normal System ◽  
High Fidelity ◽  
Aircraft Engines ◽  
Secondary Damage

A similarity-based modeling (SBM) technique is demonstrated that provides very early annunciation of the onset of gas path faults in aircraft engines. This powerful approach is shown to provide high fidelity estimates for real-time condition monitoring of aircraft engine signals. These estimates are used to detect the onset of changes in the inter-relationship between the various signals using a sophisticated set of built-in algorithms and tools. The ability of the SBM software to reliably detect subtle changes in signal behavior that are characteristic of a developing anomaly is coupled with a diagnostic rules engine to enable a rapid and robust fault recognition capability. The SBM software operates using a set of algorithms that construct a multivariate nonparametric model of the traditional monitoring sensors (pressure transducers, thermocouples, flow meters, etc.) present in the system. This model is used to generate real-time estimates of sensor values that represent normal system operation. A series of sophisticated tools compares these very high fidelity estimates to the actual sensor readings to detect discrepancies. Finally, a series of logic rules derived from a combination of engineering analysis and experience is applied to the output from the modeling engine in real-time to alert the user of developing serious conditions that need either immediate or planned maintenance attention. The software system provides a complete approach to asset monitoring that minimizes down time, maximizes availability, encodes (preserves) operator knowledge and lowers the overall costs associated with maintaining the assets. In this paper, we demonstrate the use of the similarity-based modeling approach for detecting faults in the gas path of aircraft engines. Some results from the monitoring of over 1,100 engines at a major commercial airline over a two-year period are described. Operationally, the early detection of developing engine faults has prevented delays and cancellations, and has contributed to a reduction in the airline’s in-flight shutdown rate. Financially, this approach has led to significant cost savings by the prevention of major secondary damage.

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