Honeywell International Inc.

Mitek Analytics LLC

Other

Honeywell G.m.b.h.

Dimitry Gorinevsky

Grant A Gordon

Joseph J Nutaro

Tristram Tupper Hyde

Lawrence P Davis

Mario B Ignagni

A computer-based monitoring system and monitoring method implemented in computer software for analyzing the reliability data collected in the maintenance and repair operations in a fleet of assets with parts of the same type over a period of time. The reliability data include reliability event data, such as failures, repairs, and replacements of the parts. The reliability data further include asset usage data, such as usage time, or usage missions, or usage mileage, or usage flight hours, or such. The monitoring system analyses historical reliability data to build a reliability model. This model is further used in reliability SPC algorithms detecting bad actor assets and bad actor parts that show consistently worse reliability than normal parts and assets.

System and method for fleet reliabity monitoring

A computer-based monitoring system and monitoring method implemented in computer software for detecting, estimating, and reporting the condition states, their changes, and anomalies for many assets. The assets are of same type, are operated over a period of time, and outfitted with data collection systems. The proposed monitoring method accounts for variability of working conditions for each asset by using regression model that characterizes asset performance. The assets are of the same type but not identical. The proposed monitoring method accounts for asset-to-asset variability; it also accounts for drifts and trends in the asset condition and data. The proposed monitoring system can perform distributed processing of massive amounts of historical data without discarding any useful information where moving all the asset data into one central computing system might be infeasible. The overall processing is includes distributed preprocessing data records from each asset to produce compressed data.

System and method for monitoring distributed asset data

Proposed is a method, implemented in software, for estimating fault state of an apparatus outfitted with sensors. At each execution period the method processes sensor data from the apparatus to obtain a set of parity parameters, which are further used for estimating fault state. The estimation method formulates a convex optimization problem for each fault hypothesis and employs a convex solver to compute fault parameter estimates and fault likelihoods for each fault hypothesis. The highest likelihoods and corresponding parameter estimates are transmitted to a display device or an automated decision and control system. The obtained accurate estimate of fault state can be used to improve safety, performance, or maintenance processes for the apparatus.

Method and system for diagnostics of apparatus

A structure health monitoring ('SHM') system according to the invention can be deployed in an onboard environment, such as an aircraft, to provide an ongoing damage assessment of structural components on the aircraft. The SHM system reduces or eliminates time consuming and costly manual inspections of aircraft. The SHM system can leverage known sensor technologies to collect sensor data indicative of the structural health of the monitored components. The sensor data is processed and baselined with sensor feature baselines and damage estimate baselines to provide an accurate final damage estimate for the monitored component. The final damage estimate can be further processed or formatted for compatibility with aircraft maintenance systems.

Structure health monitoring system and method

A method for estimating damage to a structure is provided. The method includes collecting a plurality of damage estimates over time and filtering the plurality of damage estimates with spatio-temporal filters. The method also includes generating an enhanced damage estimate based on the filtered damage estimate. In other embodiments, a damage estimate system and a computer readable medium having instructions embodied thereon for a method for estimating damage to a structure are provided.

Spatio-temporal filter for structural health monitoring

A system and method is provided for trending performance in a mechanical system. The trending system includes a performance estimator and a predictive trending mechanism. The performance estimator receives sensor data from the mechanical system and generates performance parameter estimates for the mechanical system. The performance parameter estimates are parametric data that are indicative of performance in the mechanical system. The performance parameter estimates are passed to a predictive trending mechanism. The predictive trending mechanism determines an estimated trend for the performance parameter estimates through monotonic regression of the performance parameter estimates. The estimated trend of the performance parameter estimates can include filtered values of the performance parameter estimates and/or predictions of future performance parameters.

Trending system and method using monotonic regression

A measurement system is provided that facilitates the measuring of a shape of a flexible electromagnetic radiation structure. The measurement system includes a plurality of local sensors, a central sensor and a measurement processor. Each of the plurality of local sensors is configured to sense the position of a portion of the flexible electromagnetic radiation structure. The central sensor is configured to determine an overall shape of the flexible electromagnetic radiation structure. The measurement processor provides the ability to combine sensor data from the local sensors and sensor data from the central sensor to provide an accurate measurement of the shape of the flexible electromagnetic radiation structure.

Measurement system for electromagnetic radiation structure

A control system is provided for controlling the shape of a flexible electromagnetic radiation structure. The control system includes a plurality of actuators and a plurality of computational elements. Each of the plurality of actuators is coupled to a portion of the radiation structure and can be selectively actuated by the plurality of computational elements. Thus, the plurality of actuators and the plurality of computational elements provide control of the flexible electromagnetic radiation structure shape. In one embodiment, each of the plurality of computational elements controls one section of the flexible electromagnetic radiation structure. Thus each of the plurality of computational elements receives controls the one or more of the plurality of actuator within its section. Together, the plurality of computational elements provide a distributed control network for the radiation structure. The control system thus provides the ability to accurately control and shape a flexible electromagnetic radiation structure.

Electromagnetic radiation structure control system

The present invention is directed to improving the accuracy with which a stationary array sensor provides cross directional measurements by providing an offset compensation to the stationary array sensor using the output of a scanning sensor associated with the manufacturing process. Exemplary embodiments correlate outputs from the stationary sensor array and the scanning array using a data reconciliation process. For example, a practical, real time data reconciliation of measurements from the scanning sensor and measurements from the stationary array sensor is achieved by computing offsets using a bank of Kalman filters to correlate outputs from the two sensors for each measurement zone, wherein each filter possesses a relatively simple computational structure. The Kalman filters can fuse the outputs from the stationary array sensor and the scanning sensor to track, and compensate, drift of the stationary array sensor.

Data fusion of stationary array sensor and scanning sensor measurements

Process control methods and apparatus for controlling batch processes including batch heating processes. The present invention includes iterative learning control (ILC) techniques to provide improved run-to-run process control. One method provides a desired temperature profile over the length of a batch time period. This method gathers historical measured value data over the length of a first batch run which can be converted to deviation or error historical data, as well as a historical output history of outputs provided to control the process for the first run. The historical deviation and output histories from the first run can then be used to generate the output profile for a second batch run. Thus, the outputs from a first batch, such as the output to a local heater control, together with the deviation or error history from the first batch run, can be added to the output value provided during the previous batch to generate new output value to be used to control a second batch run.

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Palo Alto, CA, United States of America

Dimitry Gorinevsky