Hai Qiu

Modeling Propagation of Gas Path Damage

This paper describes how damage propagation can be tracked and modeled for a range of fault modes in some modules of commercial high bypass aircraft engines. To that end, response surfaces of all sensors are generated via a thermo-dynamical simulation model for the engine (cycle deck) as a function of variations of flow and efficiency of the modules of interest. These surfaces are normalized and superimposed. Next, sensor readings are matched to those surfaces and -using an optimization approach -the corresponding flow and efficiency pair is found that best explains the sensor data. This flow and efficiency pair is then compared to previous pairs and the direction of the change as well as the rate of change is determined. The whole trajectory is then projected into the time domain. An extrapolation of the curve to the limit (which is established via operational margins) yields the remaining life. In a backward mode, the extrapolated curve is discretized and estimated future flow and efficiency pairs are retrieved. These pairs are then input to the cycle deck to produce future anticipated sensor readings as well as confirmatory trips of operational margins. Changes of the future sensor readings with real readings are used to adjust the remaining life calculations. The method is demonstrated on time series of historical engine faults.

Multivariate change detection for time series data in aircraft engine fault diagnostics

Change detection is an essential task in equipment monitoring, fault diagnostics and system prognostics. It involves monitoring change to the device state to detect faulty behavior. Early change detection can indicate abnormal conditions that can help in early fault diagnostics. This will allow for timely maintenance actions to be taken before the fault progresses, causes secondary damage to the system, and leads to equipment downtime. Approaches for change detection commonly make use of univariate techniques to detect changes in the measurement of individual sensors. However, overall system state is also characterized by the interactions and inter-relationships between the various sensor measurements considered together; univariate techniques, by focusing individually on the sensors, can miss some of the state information present in the system of sensors examined as a whole. In this paper, we briefly introduce a couple of interesting univariate change detection techniques, and then we investigate the application of three different multivariate change detection techniques. We also present results from the application of these techniques to high bypass commercial engines.

Multivariate anomaly detection in real-world industrial systems

Anomaly detection is a critical capability enabling condition-based maintenance (CBM) in complex real-world industrial systems. It involves monitoring changes to system state to detect ldquoanomalousrdquo behavior. Timely and reliable detection of anomalies that indicate faulty conditions can help in early fault diagnostics. This will allow for timely maintenance actions to be taken before the fault progresses and causes secondary damage to the system leading to downtime. When an anomaly is identified, it is important to isolate the source of the fault so that appropriate maintenance actions can be taken. In this paper, we introduce effective multivariate anomaly detection techniques and methods that allow fault isolation. We present experimental results from the application of these techniques to a high-bypass commercial aircraft engine.

On-Board Aircraft Engine Bearing Prognostics: Enveloping or FFT Analysis?

Roller bearing prognosis requires the detection of a bearing defect signature in the earliest possible stage in order to avoid a minor or catastrophic mechanical failure. Defects can occur in any of the bearing parts, inner and outer race, cage and rolling elements. It is possible to identify the defective component of the bearing based on the specific vibration frequencies that are excited. However, the pattern of vibration spectrum changes as the bearing deteriorates through different stages. Depending on which failure stage the bearing is in, different techniques are required to find fault signatures in different frequency ranges. Techniques such as enveloping analysis that works in the high frequency region require higher data sampling rates and therefore more expensive data acquisition hardware than techniques conducted in low frequency region. This paper compares two popular rolling element bearing diagnostics techniques — spectrum analysis in the bearing characteristic frequency range and enveloping analysis in the high frequency range — using aircraft engine test rig data. The techniques are compared both in terms of the time of detection and data sampling requirement; this analysis provides guidance for technology adoption in future field deployment. Results demonstrate that enveloping analysis is able to detect bearing defects much earlier than the spectrum analysis, but it requires a higher data sampling rate. The bearing defect characteristic frequency is detectable in low frequency spectrum only in the late stage of the failure and it is contaminated by other harmonics such as shaft unbalance. From a practical perspective, the final choice of the technology adopted for deployment should be based on an analysis of hardware requirements and tolerance of detection latency.Copyright

Evaluation of filtering techniques for aircraft engine condition monitoring and diagnostics

Engine condition monitoring and diagnostics are critical for safe, efficient and profitable aircraft operation. Snapshots of sensor measurements are typically used for engine health monitoring. Deviation of those measurements from a reference condition is a key feature for engine fault detection. Measurement noise and sensor failure, however, often contaminate those signals and affect performance of the fault detection. Filtering or signal smoothing is therefore a helpful technique to improve fault isolation and accuracy and robustness of the detection. Filtering is a very active area of research in the signal processing field; in the past several decades, a large variety of techniques have been developed. Each technique has its advantages and disadvantages, depending on the type of signal. The goal of this paper to review and evaluate various filtering techniques for aircraft engine condition monitoring applications, and recommend the most suitable filtering algorithms based on the unique characteristics of aircraft engine signals.

Anomaly detection using data clustering and neural networks

Anomaly detection provides an early warning of unusual behavior in units in a fleet operating in a dynamic environment by learning system characteristics from normal operational data and flagging any unanticipated or unseen patterns. For a complex system such as an aircraft engine, normal operation might consist of multiple modes in a high dimensional space. Therefore, anomaly detection approaches based on single cluster data structure will not work. This paper investigates data clustering and neural network based approaches for anomaly detection, specifically addressing the situation which normal operation might exhibit multiple hidden modes. Results show detection accuracy can be improved by data clustering or learning the data structure using neural networks.

Fusion of Vibration and On-line Oil Debris Sensors for Aircraft Engine Bearing Prognosis

This paper summarizes a sensor fusion approach for rolling-element bearing prognostics developed in the DARPA engine system prognosis (ESP) program. Bearings are critical components of an aircraft engine, so detecting a de fect as early as possible and the ability to assess the damage state in real time have a profoun d effect on both operational safety and mission success, particularly in single-engine airc raft. The fusion of vibration and oil debris information, capitalizing on the strengths of each approach, results in a sensitive and robust defect detection and assessment system. A fuzzy log ic based sensor fusion scheme was developed and evaluated experimentally on military engine differential bearings. New bearings with seeded indent defects were used to co nduct the experiments. Vibration and oil debris data were collected as the bearing spall ini tiated and propagated during each test. Fuzzy membership functions and fuzzy rules were derived from experimental data. Results demonstrate that fusion of the two signals improves the accuracy and robustness of bearing spall detection.

Estimating Deterioration Level of Aircraft Engines

This paper describes an approach to estimate the deterioration level of aircraft engines using engine monitoring data and a physics-based engine model. The estimation process is carried out by a neural network, which is trained by data generated using a physical-based engine model complemented with an empirically derived engine deterioration model. The deterioration model allows manipulation of several engine health parameters, such as module efficiency and flow capacity, to simulate engine deterioration. Simulated sensor outputs are used to build independent transfer functions relating the sensor values to a deterioration level. A calibration model corrects the sensor readings to a reference condition so that the effect of variation of operating condition is minimized. The proposed approach can be used to assess engine deterioration level in real time. The proposed deterioration estimation approach is validated using real-world engine data.Copyright