Honeywell International Inc.

Qingqiu Ginger Shao

David Michael Kolbet

Randy R Magnuson

Bradley John Barton

Bin Dong

Kyusung Kim

Gary Kumagai

Roy Araki

C Arthur Dins

Eric Shi

Wenyan Guo

Asif Khalak

Mohit Singhai

Andrew Earl

Akhilesh Maewal

Qi Li

Kelly Jean Lechtenberg

Jinkun Liu

Dave Miller

An example system for determining a performance status of an environmental control system (ECS) in a vehicle includes memory and processing circuitry. The processing circuitry is configured to determine, based on one or more of the aircraft data, the weather data, or the trending data, an estimated compressor exit temperature. The processing circuitry is configured to determine a current residual compressor exit temperature based on the estimated compressor exit temperature and a current compressor exit temperature. The processing circuitry is configured to determine whether a residual condition is met based at least in part on the residual compressor exit temperature and whether a pack condition is met based at least in part on a pack temperature. The processing circuitry is configured to provide an indication of the performance status of the ECS based on at least one of the residual condition or the pack condition being met.

Environmental control system performance monitoring

According to certain aspects of the disclosure, a computer-implemented method may be used for detecting health status of an environmental control system. The method may include receiving aircraft data of an aircraft and receiving flight data of an aircraft. Calculating a predicted performance of the aircraft based on the received aircraft data and the received flight data and generating at least one model scalar or residual, wherein the at least one model scalar or residual is generated based on the aircraft data of the aircraft. Identifying at least one pattern from the at least one model scalar or residual and classifying the at least one pattern into at least one of a plurality of classifications. Identifying a failure of modes or components from the classifications and transmitting a maintenance report once the failure of modes or components is identified.

Pattern classification system with smart data collection for environmental control system fault isolation

A vehicle management system includes an onboard vehicle system and a ground system. The onboard vehicle system includes a compressor exit temperature sensor configured to determine an ECS compressor exit temperature; a data acquisition unit coupled to receive the compressor exit temperature, an altitude of the first vehicle, and an ambient temperature and configured to generate a data report; and a communications system coupled to receive and transmit the data report. The ground system includes a ground data server coupled to receive the data report and determine a fouling condition of an environmental conditioning element of the ECS based on at least the compressor exit temperature, the altitude, and the ambient temperature; and a mobile application module coupled to receive the fouling condition from the ground data server and configured to provide an interface for displaying the fouling condition to a user on a mobile device.

Aircraft management systems and methods for ECS predictive maintenance

A maintenance system is provided for an environmental conditioning element of an environmental control system of a vehicle. The maintenance system includes a data acquisition module configured to determine an ambient temperature, an altitude of the vehicle, and a measured ECS compressor temperature; a fouling module coupled to receive the ambient temperature, the altitude, and the measured ECS compressor temperature from the data acquisition module and configured to generate a fouling condition of the environmental conditioning element based on at least the ambient temperature, the altitude, and the measured ECS compressor temperature; and a reporting module coupled to receive the fouling condition from the fouling module and configured to generate a report for a user that includes the fouling condition.

Aircraft maintenance systems and methods for ECS fouling predictions

Methods, systems and apparatus are provided for ranking tests of interest. A set of failure modes of interest and a set of tests of interest are determined. A differentiation factor is then computed for each of the tests of interest, and each of the tests of interest can then be ranked based on their respective differentiation factors.

Methods systems and apparatus for ranking tests used to identify faults in a system

Methods, systems and computing devices are provided for using a completed corrective action as evidence of a fault. The methods, systems and computing devices receive equipment status evidence and determine an equipment fault based on the equipment status evidence. The methods, systems and computing devices also create and rank a list of potential failure modes based at least in part on the determined equipment fault, recommend a corrective action to correct the equipment fault based at least in part on the ranking of the potential failure modes and receiving additional equipment status evidence indicating that the recommended corrective action failed to correct the equipment fault. The methods, systems and computing devices then associate a detection probability and a false negative rate with the failed corrective action to create additional status evidence, and re-rank the list of potential failure modes for subsequent performance based on the additional status evidence.

Systems and methods for using a corrective action as diagnostic evidence

A method of detecting and diagnosing system faults, includes detecting the noisy status of a monitor during operations and incorporating a quantified monitor uncertainty level to support fault isolation reasoning. A sequential probability ratio test is used to statistically test the noisy status of a monitor and Shannon's entropy theory is used to quantify the uncertainty levels of the monitor to support the use of the monitor values in fault isolation.

Noisy monitor detection and intermittent fault isolation

A method for determining relative likelihood of a failure mode is provided. The method comprises receiving evidence observations of a monitored system from monitors connected in a many-to-many relationship to the failure modes, generating a fault condition including states of all failure modes that are connected to the monitors, and computing a relative probability of failure for each failure mode. The fault condition is generated for a reference model of the monitored system and is based on the received evidence observations. The relative probability of failure for each failure mode is based on a false alarm probability, a detection probability, and a ratio of prior probabilities of a candidate hypothesis to a null hypothesis of no active failure mode.

Method for computing the relative likelihood of failures

The present application relates to a method of splitting a fault condition including receiving evidence observations of a monitored system from monitors connected in a many-to-many relationship to the failure modes, generating a fault condition, computing a relative probability of failure for each failure mode in the fault condition. When there is more than one failure mode in the fault condition, the method includes computing a relative probability of each pair of failure modes in the fault condition, ranking the computed relative probabilities of the individual failure modes and the computed relative probabilities of the pairs of failure modes. If the highest ranked failure mode is a pair of failure modes, the fault condition is split based on the failure modes in the highest ranked pair of failure modes are split. If the highest ranked failure mode is an individual failure mode, a failure is isolated based on the ranking.

Fault splitting algorithm

A method for determining a probabilistic loss of function of a system includes the steps of determining a plurality of failure mode probabilities, ranking a plurality of functions pertaining to the failure mode probabilities, and identifying a likely function at least substantially lost by the system based at least in part on the plurality of failure mode probabilities and the ranking.

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Oro Valley, AZ, United States of America

Qingqiu Ginger Shao