OIL DEBRIS MONITORING FOR FAILURE DETECTION ISOLATION

A method of detecting debris within a lubricant stream, the method includes generating data indicative of debris and sensor system functionality within a lubricant stream with a sensor system. The data is communicated to a controller. Features calculated from the data are indicative of a debris within the lubricant stream. The calculated features are compiled over time during operation. The compiled features are classified. An oil debris monitoring system for a turbine engine is also disclosed.

BACKGROUND

Lubricant is provided throughout the engine and is circulated through various compartments and structures. Engine lubricant and oil is typically inspected during periodic maintenance for indications of component degradation or failure. Debris in the form of metal particles or other contaminants found in the lubricant can be traced back to a particular component to trigger further inspection. Such periodic monitoring is useful but may not be sufficiently timely to detect potential issues before less costly correction efforts can be utilized. A debris detection system is sometime utilized to detect potential issues based on information obtained from a particle sensor. Debris detection systems includes sensors that are susceptible to providing different readings dependent on different engine operating conditions or environmental conditions. Such variations in sensor data can limit effectiveness. Additionally, errors or failures generated by the detection system can result in false or unreliable information.

Turbine engine manufacturers continue to seek further improvements to engine performance including improvements to thermal, transfer and propulsive efficiencies.

SUMMARY

In a featured embodiment, a method of detecting debris within a lubricant stream, the method includes generating data indicative of debris and sensor system functionality within a lubricant stream with a sensor system. The data is communicated to a controller. Features calculated from the data are indicative of a debris within the lubricant stream. The calculated features are compiled over time during operation. The compiled features are classified.

In another embodiment according to the previous embodiment, classifying the compiled features includes determining an occurrence rate for the features.

In another embodiment according to any of the previous embodiments, includes comparing the determined occurrence rate with an expected range, and identifying an engine operational characteristic responsive to the comparison with the expected range.

In another embodiment according to any of the previous embodiments, the features include a plurality of features of the lubricant stream that are detected and identified concurrently.

In another embodiment according to any of the previous embodiments, the features include at least one of a time-rate of generation and an overall accumulated number of occurrences.

In another embodiment according to any of the previous embodiments, a time-rate of generation that corresponds with a desired operation of a turbine engine is compared to the determined time-rate of generation to identify an engine operational characteristic.

In another embodiment according to any of the previous embodiments, compiling the features is performed during operation of a turbine engine for analysis during a non-operating condition.

In another embodiment according to any of the previous embodiments, includes analysis of the compiled features for indications of debris within the lubricant stream indicative of a mechanical anomaly.

In another embodiment according to any of the previous embodiments, includes analysis of the compiled features for indications of an anomaly in operation of the sensor system.

In another featured embodiment, an oil debris monitoring system for a turbine engine includes a sensor assembly mounted proximate to a lubricant conduit for generating a signal indicative of particles within a lubricant stream flowing through the lubricant conduit. A feature generator is configured to receive the signal from the sensor assembly and generate a data stream including features representing characteristics of the lubricant stream. A feature accumulator is configured to compile the features during an operational period of the turbine engine and storing the compiled features in a memory device. A fault isolator is configured to classify the compiled features after the operational period is concluded and generate an output identifying anomalies in the features.

In another embodiment according to the previous embodiment, the fault isolator identifies anomalies indicative of a fault in the sensor assembly.

In another embodiment according to any of the previous embodiments, the fault isolator classifies the features according to an occurrence rate for the features.

In another embodiment according to any of the previous embodiments, the features include a plurality of features of the lubricant stream that are detected and identified concurrently.

In another embodiment according to any of the previous embodiments, the features include at least one of a time-rate of generation and an overall accumulated number of occurrences.

In another embodiment according to any of the previous embodiments, the feature generator and feature accumulator are part of a controller of the turbine engine.

In another embodiment according to any of the previous embodiments, classifying the compiled features includes determining an occurrence rate for the features.

In another embodiment according to any of the previous embodiments, includes comparing a determined occurrence rate with an expected range, and identifying an engine operational characteristic responsive to the comparison with the expected range.

In another embodiment according to any of the previous embodiments, the features include a plurality of features of the lubricant stream that are detected and identified concurrently.

In another embodiment according to any of the previous embodiments, the features include at least one of a time-rate of generation and an overall accumulated number of occurrences.

In another embodiment according to any of the previous embodiments, a time-rate of generation that corresponds with a desired operation of the turbine engine is compared to the determined time-rate of generation to identify an operational characteristic.

DETAILED DESCRIPTION

A mid-turbine frame58of the engine static structure36is arranged generally between the high pressure turbine54and the low pressure turbine46. The mid-turbine frame58further supports bearing systems38in the turbine section28as well as setting airflow entering the low pressure turbine46.

In one disclosed embodiment, the gas turbine engine20includes a bypass ratio greater than about ten (10:1) and the fan diameter is significantly larger than an outer diameter of the low pressure compressor44. It should be understood, however, that the above parameters are only exemplary of one embodiment of a gas turbine engine including a geared architecture and that the present disclosure is applicable to other gas turbine engines.

Gas turbine engines designs are seeking to increase not only overall efficiency but also to increase engine monitoring capabilities to detect potential problems to enable corrective action prior to engine function being adversely effected. The example gas turbine engine includes an oil debris monitoring (ODM) system62that detects contaminants within a lubricant stream68. It should be appreciated that throughout this application the terms “oil” and “lubricant” are used interchangeable.

The ODM system62includes at least one sensor assembly64disposed within a lubricant passage that provides information to a controller66. The controller66may be an overall engine controller or an independent controller. The ODM system62detects debris and provides information indicative of debris to the controller66. Depending on the contaminants detected in the lubricant stream, the controller may prompt a further inspection or other maintenance activity.

Referring toFIG. 2with continued reference toFIG. 1, the example ODM system62includes the sensor assembly64that surrounds or is part of a conduit70through which the lubricant stream68flows. The lubricant stream68may include contaminants schematically illustrated at72. The contaminants72come from various locations in the engine that are supplied oil and can indicate potential mechanical issues that require attention. The contaminants72can be identified by their material characteristics, shape, size or other features sensed by the sensor64. The sensor64provides an output signal74that is sent to the controller66. The controller66generates data from the signal74that is compiled for latter analysis to determine operational health of the turbine engine10.

In this example, the sensor64generates an electrical signal that varies due to contaminants72within the lubricant stream68. The signal74varies based on the electrical characteristics of the lubricant stream68. The electrical characteristics of the lubricant stream68vary due to different types of contaminants72. The contaminants72may be ferrous or non-ferrous and may vary in size, shape and quantity for a given time.

The example sensor64uses electrical characteristics of the lubricant stream74to generate the signal74, however, other sensors that generate signals that change depending on the presence of contaminants72within the lubricant stream are also within the contemplation of this disclosure and may be utilized in the disclosed oil debris monitoring system62.

In previous oil debris monitoring systems, the information provided by the sensor64was processed immediately to identify specific contaminant characteristics and thereby identify specific types of contaminants within the stream. A discrete and separate amount of data would be analyzed individually to determine a specific characteristic that identified with a known contaminant material. Such prior systems detect specific types of particles in the lubricant stream68based on a preset definitions for contaminant types. These preset definitions were not always able to account for variations and characteristics of contamination that were detected during different engine operating conditions. As appreciated, each gas turbine engine may include different types of contaminants based on specific operating conditions and environment. A fixed or standard set of conditions is difficult to develop that efficiently covers all types of engines in every type of environment.

Referring toFIG. 3with continued reference toFIG. 1, the example ODM system62processes and detects particles and contaminants within a lubricant stream in a manner that accommodates variation in engines and operational environments. Moreover the disclosed ODM system62generates multiple calculated statistical features from the sensor signal74to enable subsequent analysis, classification and anomaly detection.

The example system62includes at least one sensor assembly64that generates a signal74that is sent to a controller66. The example controller66is mounted within an aircraft including the turbine engine20. It is within the contemplation of this disclosure that the controller66may be a separate dedicated controller or part of the engine controller.

The controller66includes a feature generator76that receives the signal74and generates a feature, illustrated schematically and indicated at82, from the signal that are indicative of some characteristic of the lubricant flow68. The features generated are statistical properties that are calculated and determined from the sensor signal76. The features calculated are selected depending on the available data and the desired characteristic of interest. The features calculated can include frequency content of the signals, projection of the signal onto a wavelet or other basic functions including local peak maximums and minimums, local root mean square, variance, skewness, kurtosis, and other higher statistical moments as are known and understood in the art.

The controller66further includes a feature accumulator78that compiles the calculated features82as is schematically shown at84. The features82are compiled over time for off-line processing. The statistics of each feature82may be compiled according to a time-rate of generation and/or for an overall accumulated number of occurrences. Some of the features82are associated with normal healthy engine operation, while other features82may be indicative of a possible mechanical problem.

A fault isolator80is illustrated schematically and receives the compiled features84from the feature accumulator78. The fault isolator78classifies the compiled features84off-line. Off-line is utilized to indicate a period of time when data is not being generated by the sensor64and is most often associated with the completion of a flight cycle where the engine20is shut down. The fault isolator80may be part of the controller66or maybe a separate system utilized for the analysis of the accumulated features84. The fault isolator80classifies the accumulated features84for analysis and to drive required maintenance activities. Anomalies in the features84can be indicative of mechanical issues with the engine20or faults in the sensor assembly64.

Operation of the example ODM system62includes the first step of detecting particles and contaminants within a lubricant stream with the sensor assembly64schematically indicated at88. The sensor assembly64generates a signal indicative of debris72in the lubricant stream68. In the disclosed example, the sensor assembly64generates an electrical signal indicative of a charge that varies based on electrical characteristics of the lubricant stream as schematically shown at86. The data signal74from the sensor64is a continuous stream of data that continues uninterrupted during operation.

The data signal74is received by the feature generator76that performs the next step indicated at90of generating statistical features that represent a condition and content of the lubricant stream68over time. The feature generator76processes the signal74continually to generate a plurality of different statistical features (one shown schematically at82) that correspond to different characteristics of interest of the lubricant stream68. The features that are generated from the signal74are in the form of statistically calculated properties that can include any known statistical parameter and/or feature. The calculated features can include frequency content of the signals, projection of the signal onto a wavelet or other basic functions including local peak maximums and minimums, local root mean square, variance, skewness, kurtosis, and other higher statistical moments as are known and understood in the art. The feature generator calculates continuously, concurrently and independently multiple features that are subsequently categorization off-line for detection of anomalies.

The calculated features82are combined in the feature accumulator78over time during operation as is schematically indicated at92. The features82are compiled and/or combined in different ways to reflect engine operation and to group relevant and similar information. The compiling and/or combining of features may be performed according to time-rate of generation for each feature, or a according to an accumulated number of occurrences for latter off-line processing.

The compiled features84are next analyzed by the fault isolator80as is indicated at94. Analysis may be performed during operation of the engine, during non-operating conditions, and/or after expiration of a set time. Non-operating conditions can include engine idling and periods during operation after a period in which no issues are detected. The fault isolator80is where a comparison of the accumulated features84are finally compared to expected ranges indicative of mechanical failure or of sensor malfunction. Because the accumulated features84are analyzed off-line, they can reflect an entire operating cycle of the engine and are therefore better suited to identify specific anomalies in particle and contaminants within the lubricant stream68. Moreover, the off-line analysis also provides a means of identifying a baseline of engine operation that corresponds with healthy engine operating conditions.

The off-line analysis includes classification of the compiled features as is schematically indicated at98. In this example, classification includes determining an occurrence rate for the calculated features82. The determined occurrence rate is reviewed to determine how it compares to an expected range. The occurrence rate corresponds with an identified engine operational characteristic and a corresponding directive for a maintenance activity.

Furthermore, noise or interference for a specific engine can be identified and filtered. In previous systems, noise and interference specific to a unique operating environment or engine would cloud analysis and reduce effectiveness. The example system provides continuous and complete features compilations that enable baseline determination for each specific engine and operating environment. Thus, enabling detection of both mechanical anomalies indicative of actual issues with the engine and anomalies that are indicative of sensor faults.

Once the compiled features are classified and analyzed for anomalies, the ODM system62provides an output that drives a desired corrective maintenance activity indicated at96. The activity96may range from a further inspection to replacement of an engine component or attention to the sensor assembly64.

Accordingly, the example system concurrently calculates all features and pertinent statistics and delays the classification and anomaly detection until the system is offline and no longer actively monitoring for particles in real time. By performing the anomaly detection and classification after the system is offline, the systems availability to detect particles is increased. Moreover, because the system is continually generating data it is possible to determine and identify failures for intermittent signal or sensor failures of the system.