Apparatuses, computer-implemented methods, and computer program products for improved asset degradation monitoring and prediction

Patent metadata

Field	Value
Publication number	US-2024005287-A1
Application number	US-202218059804-A
Country	US
Kind code	A1
Filing date	Nov 29, 2022
Priority date	Jun 30, 2022
Publication date	Jan 4, 2024
Grant date	—

Abstract

Embodiments of the disclosure provide for determination and/or notifying of when an asset requires maintenance. Such embodiments enable outputting notifications in circumstances where tracked data for operations of an asset indicate or are predicted to violate particular thresholds. Some embodiments receive a feedback data set for an asset, identify a command data set for the asset, determine a delay value based at least in part on the feedback data set and the command data set, determine a command-feedback difference value based at least in part on the feedback data set and the command data set, and output a degradation indicator based at least in part on the delay value and the command-feedback difference value.

First claim

What is claimed is:

1 . An apparatus for indicating asset degradation, the apparatus comprising at least one processor and at least one non-transitory memory including computer-coded instructions thereon, the computer coded instructions, with the at least one processor, cause the apparatus to:
identify a command data set associated with the asset; identify a feedback data set associated with an asset; determine a delay value associated with the command event based at least in part on the feedback data set; determine a command-feedback difference value based at least in part on a first portion of feedback data from the feedback data set and a first portion of command data set, the first portion of the feedback data and the first portion of the command data associated with at least one timestamp determined as after an end of a command event; and output a degradation indicator based at least in part on the delay value and/or the command-feedback difference.

2 . The apparatus according to claim 1 , the apparatus further caused to:
determine delay status data based at least in part on comparison between the delay value and a delay threshold; and output the degradation indicator based at least in part on the delay status data.

3 . The apparatus according to claim 1 , the apparatus further caused to:
determine difference status data based at least in part on comparison between the command-feedback difference value and a difference threshold; and output the degradation indicator based at least in part on the difference status data.

4 . The apparatus according to claim 1 , wherein to identify the feedback data set, the apparatus is caused to:
capture, via at least one sensor corresponding to the asset, feedback data indicating a current operational value for an operational aspect of the asset.

5 . The apparatus according to claim 4 , wherein the at least one sensor capture the feedback data at a sampling rate based at least in part on an asset type associated with the asset.

6 . The apparatus according to claim 1 , the apparatus further caused to:
determine, based at least in part on the delay value, a normalized delay value; and output the degradation indicator based at least in part on the normalized delay value.

7 . The apparatus according to claim 1 , the apparatus further caused to:
generate delay trend data based at least in part on the delay value; and generating, using a threshold timing prediction model, a predicted time remaining until a delay threshold is satisfied based at least in part on the delay trend data.

8 . The apparatus according to claim 7 , wherein the threshold timing prediction model comprises at least one machine learning model trained to generate, based at least in part on the delay value, the predicted time remaining until the delay threshold is satisfied.

9 . The apparatus according to claim 1 , the apparatus further caused to:
generate difference trend data based at least in part on the command-feedback difference value; and generating, using a threshold timing prediction model, a predicted time remaining until a difference threshold is satisfied based at least in part on the difference trend data.

10 . The apparatus according to claim 9 , wherein the threshold timing prediction model comprises at least one machine learning model trained to generate, based at least in part on the command-feedback difference value, the predicted time remaining until the difference threshold is satisfied.

11 . The apparatus according to claim 1 , the apparatus further caused to:
determining an update ending feedback data from the feedback data set, the update ending feedback data indicating the end of the command event; and determine the delay value based at least in part on the update ending feedback data.

12 . The apparatus according to claim 11 , wherein determining the update ending feedback data comprises:
determining a first timestamp associated with a first data value, wherein, after the first timestamp, data values in the feedback data set remain within a tolerance threshold for a timestamp interval that satisfies a value updating time threshold.

13 . The apparatus according to claim 1 , wherein the command data set comprises data values within a range of operable values associated with the asset.

14 . The apparatus according to claim 1 , wherein the feedback data set comprises feedback data representing a speed of operation of the asset or a position of operation of the asset.

15 . The apparatus according to claim 1 , wherein to output the degradation indicator, the apparatus is caused to:
cause output of a maintenance notification to a user device associated with the asset, the maintenance notification indicating that the asset requires maintenance or a time remaining until the asset should undergo maintenance.

16 . A computer-implemented method comprising:
identifying a command data set associated with the asset; identifying a feedback data set associated with an asset; determining a delay value associated with the command event based at least in part on the feedback data set; determining a command-feedback difference value based at least in part on a first portion of feedback data from the feedback data set and a first portion of command data set, the first portion of the feedback data and the first portion of the command data associated with at least one timestamp determined as after an end of a command event; and outputting a degradation indicator based at least in part on the delay value and/or the command-feedback difference.

17 . The computer-implemented method according to claim 16 , the computer-implemented method further comprising:
determining, based at least in part on the delay value, a normalized delay value; and outputting the degradation indicator based at least in part on the normalized delay value.

18 . The computer-implemented method according to claim 16 , the computer-implemented method further comprising:
generating delay trend data based at least in part on the delay value; and generating, using a threshold timing prediction model, a predicted time remaining until a delay threshold is satisfied based at least in part on the delay trend data.

19 . A computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon that, in execution with at least one processor, is configured for:
identifying a command data set associated with the asset; identifying a feedback data set associated with an asset; determining a delay value associated with the command event based at least in part on the feedback data set; determining a command-feedback difference value based at least in part on a first portion of feedback data from the feedback data set and a first portion of command data set, the first portion of the feedback data and the first portion of the command data associated with at least one timestamp determined as after an end of a command event; and outputting a degradation indicator based at least in part on the delay value and/or the command-feedback difference.

20 . The computer program product according to claim 19 , the computer program product further configured for:
determining, based at least in part on the delay value, a normalized delay value; and outputting the degradation indicator based at least in part on the normalized delay value.

Assignees

• Honeywell Int Inc

Inventors

• Sikora Marek
• Jerhotova Eva
• Schindler Zdenek
• Rojicek Jiri
• Malaník Jakub
• Dimartino Jr Frank P

Classifications

• G06Q10/20 Primary

  Administration of product repair or maintenance · CPC title

• G05B23/0221 Primary

  Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods · CPC title

• G05B23/0283

  Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] (preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06) · CPC title