Integration of On-Line and Off-Line Diagnostic Algorithms for Aircraft Engine Health Management

This paper investigates the integration of on-line and off-line diagnostic algorithms for aircraft gas turbine engines. The on-line diagnostic algorithm is designed for in-flight fault detection. It continuously monitors engine outputs for anomalous signatures induced by faults. The off-line diagnostic algorithm is designed to track engine health degradation over the lifetime of an engine. It estimates engine health degradation periodically over the course of the engine s life. The estimate generated by the off-line algorithm is used to update the on-line algorithm. Through this integration, the on-line algorithm becomes aware of engine health degradation, and its effectiveness to detect faults can be maintained while the engine continues to degrade. The benefit of this integration is investigated in a simulation environment using a nonlinear engine model

Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of an Enhanced System

In this paper, an enhanced on-line diagnostic system which utilizes dual-channel sensor measurements is developed for the aircraft engine application. The enhanced system is composed of a nonlinear on-board engine model (NOBEM), the hybrid Kalman filter (HKF) algorithm, and fault detection and isolation (FDI) logic. The NOBEM provides the analytical third channel against which the dual-channel measurements are compared. The NOBEM is further utilized as part of the HKF algorithm which estimates measured engine parameters. Engine parameters obtained from the dual-channel measurements, the NOBEM, and the HKF are compared against each other. When the discrepancy among the signals exceeds a tolerance level, the FDI logic determines the cause of discrepancy. Through this approach, the enhanced system achieves the following objectives: 1) anomaly detection, 2) component fault detection, and 3) sensor fault detection and isolation. The performance of the enhanced system is evaluated in a simulation environment using faults in sensors and components, and it is compared to an existing baseline system

Enhanced Bank of Kalman Filters Developed and Demonstrated for In-Flight Aircraft Engine Sensor Fault Diagnostics

In-flight sensor fault detection and isolation (FDI) is critical to maintaining reliable engine operation during flight. The aircraft engine control system, which computes control commands on the basis of sensor measurements, operates the propulsion systems at the demanded conditions. Any undetected sensor faults, therefore, may cause the control system to drive the engine into an undesirable operating condition. It is critical to detect and isolate failed sensors as soon as possible so that such scenarios can be avoided. A challenging issue in developing reliable sensor FDI systems is to make them robust to changes in engine operating characteristics due to degradation with usage and other faults that can occur during flight. A sensor FDI system that cannot appropriately account for such scenarios may result in false alarms, missed detections, or misclassifications when such faults do occur. To address this issue, an enhanced bank of Kalman filters was developed, and its performance and robustness were demonstrated in a simulation environment. The bank of filters is composed of m + 1 Kalman filters, where m is the number of sensors being used by the control system and, thus, in need of monitoring. Each Kalman filter is designed on the basis of a unique fault hypothesis so that it will be able to maintain its performance if a particular fault scenario, hypothesized by that particular filter, takes place

Stochastic ranking process with time dependent intensities

We consider the stochastic ranking process with the jump times of the particles determined by Poisson random measures. We prove that the joint empirical distribution of scaled position and intensity measure converges almost surely in the infinite particle limit. We give an explicit formula for the limit distribution and show that the limit distribution function is a unique global classical solution to an initial value problem for a system of a first order non-linear partial differential equations with time dependent coefficients

Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of a Baseline System

In this paper, a baseline system which utilizes dual-channel sensor measurements for aircraft engine on-line diagnostics is developed. This system is composed of a linear on-board engine model (LOBEM) and fault detection and isolation (FDI) logic. The LOBEM provides the analytical third channel against which the dual-channel measurements are compared. When the discrepancy among the triplex channels exceeds a tolerance level, the FDI logic determines the cause of the discrepancy. Through this approach, the baseline system achieves the following objectives: (1) anomaly detection, (2) component fault detection, and (3) sensor fault detection and isolation. The performance of the baseline system is evaluated in a simulation environment using faults in sensors and components

Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions

GT2003-38550 APPLICATION OF A BANK OF KALMAN FILTERS FOR AIRCRAFT ENGINE FAULT DIAGNOSTICS

ABSTRACT In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated. NOMENCLATURE INTRODUCTION Fault detection and isolation (FDI) logic plays a critical role in enhancing the safety and reliability and reducing the operating cost of aircraft propulsion systems. However, achieving the FDI task with high reliability is a challenging problem. Faults may occur in various degrees of severity at various locations; therefore numerous fault scenarios are possible. Moreover, the engine's complex structure and harsh operating environment make interpretation of the available information difficult. To deal with such problems, the introduction of analytical redundancy has become common and is considered more cost-effective than hardware redundancy. With the increase of digital computational power, more sophisticated approaches such as the utilization of an on-board engine model have become possible. When a fault occurs, the first step is to detect it as soon as possible so that the scenario of a minor fault escalating to a more serious failure can be avoided. The approach commonly used for model-based fault detection is composed of two steps: 1) generate residual signals from the sensor measurements and their estimated values, and then 2) compare the residuals with thresholds to make fault detection decision

Alleviation of allergic conjunctivitis by (±)5(6)-dihydroxy-8Z,11Z,14Z,17Z-eicosatetraenoic acid in mice

Background: Allergic conjunctivitis (AC) is a common ophthalmologic disorder that causes symptoms that often reduces a patient’s quality of life (QOL). We investigated the effects of the eicosapentaenoic acid metabolite (±)5(6)-dihydroxy-8Z,11Z,14Z,17Z-eicosatetraenoic acid ((±)5(6)-DiHETE) on AC using a mouse model.Methods: BALB/c mice were sensitized with two injections of short ragweed pollen in alum, challenged fifth with pollen in eyedrops. The clinical signs and tear volume were evaluated at 15 min after the final challenge. Histamine-induced ocular inflammation model was prepared by instilling histamine onto the surface of the eye. Fifteen minutes after histamine application, tear volume was measured using the Schirmer tear test. Miles assay was performed to investigate vascular permeability. To cause scratching behavior 10 μg of serotonin was injected in the cheek.Results: Repeated topical application of pollen induced conjunctivitis, accompanied by eyelid edema and tearing in mice. Pollen application typically degranulates mast cells and recruits eosinophils to the conjunctiva. Intraperitoneal administration of 300 μg/kg of (±)5(6)-DiHETE significantly inhibited pollen-induced symptoms. The administration of (±)5(6)-DiHETE also attenuated mast cell degranulation and eosinophil infiltration into the conjunctiva. To assess the effects of (±)5(6)-DiHETE on the downstream pathway of mast cell activation in AC, we used a histamine-induced ocular inflammation model. Topical application of 4 μg/eye histamine caused eyelid edema and tearing and increased vascular permeability, as indicated by Evans blue dye extravasation. Intraperitoneal administration of 300 μg/kg or topical administration of 1 μg/eye (±)5(6)-DiHETE inhibited histamine-induced manifestations. Finally, we assessed the effects of (±)5(6)-DiHETE on itching. An intradermal injection of 10 μg serotonin in the cheek caused scratching behavior in mice. Intraperitoneal administration of 300 μg/kg (±)5(6)-DiHETE significantly inhibited serotonin-induced scratching.Conclusion: Thus, (±)5(6)-DiHETE treatment broadly suppressed AC pathology and could be a novel treatment option for AC

Marker-less tracking system for multiple mice using Mask R-CNN

Although the appropriate evaluation of mouse behavior is crucial in pharmacological research, most current methods focus on single mouse behavior under light conditions, owing to the limitations of human observation and experimental tools. In this study, we aimed to develop a novel marker-less tracking method for multiple mice with top-view videos using deep-learning-based techniques. The following stepwise method was introduced: (i) detection of mouse contours, (ii) assignment of identifiers (IDs) to each mouse, and (iii) correction of mis-predictions. The behavior of C57BL/6 mice was recorded in an open-field arena, and the mouse contours were manually annotated for hundreds of frame images. Then, we trained the mask regional convolutional neural network (Mask R-CNN) with all annotated images. The mouse contours predicted by the trained model in each frame were assigned to IDs by calculating the similarities of every mouse pair between frames. After assigning IDs, correction steps were applied to remove the predictive errors semi-automatically. The established method could accurately predict two to four mice for first-look videos recorded under light conditions. The method could also be applied to videos recorded under dark conditions, extending our ability to accurately observe and analyze the sociality of nocturnal mice. This technology would enable a new approach to understand mouse sociality and advance the pharmacological research