Emmanuel Obiesie Nwadiogbu

Model-based diagnostics for an aircraft auxiliary power unit

This paper describes a case study of model-based diagnostics system development for an aircraft auxiliary power unit (APU) turbine system. The off-line diagnostics algorithms described in the paper work with historical data of a flight cycle. The diagnostics algorithms use detailed engine systems models and fault model knowledge available to Honeywell as the engine manufacturer. The developed algorithms provide fault condition estimates that allow for consistent detection of incipient performance faults and abnormal conditions.

Fault detection and diagnosis in turbine engines using fuzzy logic

In this paper, we present a fuzzy logic based method of fault detection and diagnosis in gas turbine engines. The fuzzy logic system rule base is derived using heuristics extracted from designed experiments and flight data representing component performance changes due to field service degradation. The fuzzy logic rule based method incorporates both sensed engine parameters that represent non-deteriorated engine operation and fault conditions related to engine performance such as high pressure turbine, high pressure compressor and combustor deterioration. The fuzzy logic system is evaluated using residuals calculated based on both empirical models as inputs. The efficacy of the fuzzy logic system in detecting and diagnosing engine faults is demonstrated using field test data. We also examine performance robustness in the presence of varying levels of sensor noise and measurement errors.

Fault diagnosis in gas turbine engines using fuzzy logic

This paper describes a fuzzy logic-based method of fault detection and diagnosis in gas turbine engines. The fuzzy logic rule base is derived using heuristics based on designed experiments and flight data. The method is evaluated using model-based residuals and calculated values as inputs. The efficacy of the system is demonstrated using flight data. This paper describes how to augment a limited number of input parameters by combining them with the rates of change of the normal input parameters and other derived parameters. This augmented parameter set enables a better estimate of the prediction horizon for diagnosis. The paper also presents a case study where high-pressure spool deterioration is detected about two months prior to engine failure. Although, the system is demonstrated using the example of high pressure spool deterioration it can be applied to engine failures with similar characteristics.

Incipient Fault Detection and Diagnosis in Turbine Engines Using Hidden Markov Models

Incipient fault detection and diagnosis in turbine engines is key to effective maintenance and improved availability of systems dependent on these engines. In this paper, we present a novel method for incipient fault detection and diagnosis using Hidden Markov Models (HMMs). In particular, we focus on engine faults that are manifest in transient operating conditions such as engine startup and acceleration. HMMs are stochastic signal models that are effective in modeling transient signals. They are developed with engine data collected under nominal operating conditions. Engine data representing different fault conditions are used to develop the fault HMMs; a separate model is developed for each of the faults. Once the nominal and fault HMMs are developed, new engine data collected from the engine are evaluated against the HMMs and a determination is made whether a fault is indicated. Here, we demonstrate our HMM-based fault detection and diagnosis approach on engine speed profiles taken from a real engine. Further, the effectiveness of the HMM-based approach is compared with a neural-network-based approach and a method based on using principal component analysis in conjunction with a neural network approach.Copyright

Fault diagnosis in turbine engines using unsupervised neural networks technique

A fault diagnosis system based on the neural networks clustering technique is developed for a mid-sized jet propulsion engine. The currently recorded data set for this engine has several limitations in its quality, which results in the lack of information required for the incipient fault detection and wide coverage of failure modes. Using the residuals of core speed, exhausted gas temperature and fuel flow, the developed system is designed to diagnose the failures related to combustor liner, bleed band, and exhausted gas temperature (EGT) sensor rake. The fault diagnosis system reports not only the machine condition but also the belief factor convincing the diagnostic decisions. In this work the actual flight data collected in the field is used to develop and validate the system, and the results are shown for the test on five engines which had experienced three different failures. The presented system is implemented in the form of web-based service and has demonstrated its robustness by isolating the failures successfully in the field.

Model-based diagnostics for small-scale turbomachines

This paper describes a case study of model-based diagnostics system development for an aircraft auxiliary power unit (APU) turbine system. The off-line diagnostics algorithms described in the paper work with historical data of a flight cycle. The diagnostics algorithms use detailed turbine engine systems models and fault model knowledge available to an engine manufacturer. The developed algorithms provide fault condition estimates and allow for consistent detection of incipient performance faults and abnormal conditions.

Neural-networks-based sensor validation and recovery methodology for advanced aircraft engines

Within the context of preventive health maintenance in complex engineering systems, novel sensor fault detection methodologies are developed for an aircraft auxiliary power unit. Promising results at operational and sensor failure conditions are obtained for temperature and pressure sensors. In the methodology proposed, first covariance and noise analyses of sensor data are performed. Next, auto- associative and hetero-associative neural networks for sensor validation are designed and trained. These neural networks are used together to provide validation for pressure and temperature sensors. The last step consists of development of detection and identification logic for sensor faults. In spite o high noise levels, the methodology is shown to be very robust. More than 90% correct sensor failure detection is achieved when noise on the order of noise inherently present in sensor readings is added.