Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.