GB2624810A - Prediction of dewar failure - Google Patents

Prediction of dewar failure Download PDF

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Publication number
GB2624810A
GB2624810A GB2403123.9A GB202403123A GB2624810A GB 2624810 A GB2624810 A GB 2624810A GB 202403123 A GB202403123 A GB 202403123A GB 2624810 A GB2624810 A GB 2624810A
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United Kingdom
Prior art keywords
shipper
failure
sensor
prediction system
likelihood
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
GB2403123.9A
Other versions
GB202403123D0 (en
Inventor
Vahid Amir
Bollinger Bret
Schlesinger Phil
Exline Chris
Misra Ashish
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Cryoport Inc
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Cryoport Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US17/477,381 external-priority patent/US20230082374A1/en
Application filed by Cryoport Inc filed Critical Cryoport Inc
Publication of GB202403123D0 publication Critical patent/GB202403123D0/en
Publication of GB2624810A publication Critical patent/GB2624810A/en
Pending legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/0832—Special goods or special handling procedures, e.g. handling of hazardous or fragile goods
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
    • F17C—VESSELS FOR CONTAINING OR STORING COMPRESSED, LIQUEFIED OR SOLIDIFIED GASES; FIXED-CAPACITY GAS-HOLDERS; FILLING VESSELS WITH, OR DISCHARGING FROM VESSELS, COMPRESSED, LIQUEFIED, OR SOLIDIFIED GASES
    • F17C3/00—Vessels not under pressure
    • F17C3/02—Vessels not under pressure with provision for thermal insulation
    • F17C3/08—Vessels not under pressure with provision for thermal insulation by vacuum spaces, e.g. Dewar flask
    • F17C3/085—Cryostats
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/0835—Relationships between shipper or supplier and carriers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance

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  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Economics (AREA)
  • Software Systems (AREA)
  • Human Resources & Organizations (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Artificial Intelligence (AREA)
  • Marketing (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • Operations Research (AREA)
  • Development Economics (AREA)
  • Computational Linguistics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Thermal Sciences (AREA)
  • Mechanical Engineering (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

Method, system, apparatus, and/or device for predicting the failure of a shipper. The failure prediction system includes a first sensor configured to detect or measure first sensor data. The failure prediction system includes a memory configured to store a dewar failure model that models a failure of various shippers given one or more constraints. The failure prediction system includes a processor coupled to the memory and the first sensor. The processor is configured to estimate or predict a probability or a likelihood that a shipper will fail before or during a subsequent shipment of the shipper based on the first sensor data and the dewar failure model. The processor is configured to provide the estimated probability or likelihood that the shipper will fail before or during the subsequent shipment of the shipper.

Claims (20)

1. A failure prediction system, comprising: a first sensor configured to detect or measure first sensor data; a memory configured to store a dewar failure model that models a failure of various shippers given one or more constraints; and a processor coupled to the memory and the first sensor and configured to: estimate or predict a probability or a likelihood that a shipper will fail before or during a subsequent shipment of the shipper based on the first sensor data and the dewar failure model, and provide the estimated probability or likelihood that the shipper will fail before or during the subsequent shipment of the shipper.
2. The failure prediction system of claim 1, further comprising: a display configured to output the estimated probability or likelihood that the shipper will fail before or during the subsequent shipment of the shipper.
3. The failure prediction system of claim 1, further comprising: a user interface configured receive user input that indicates whether the shipper failed before, during or after the subsequent shipment of the shipper; wherein the processor is configured to: update the dewar failure model based on the user input and the sensor data in real-time.
4. The failure prediction system of claim 1, wherein the sensor includes at least one of a temperature sensor, a shock or vibration sensor, or a pressure sensor and the sensor data includes at least one of a temperature within the shipper, shocks or vibrations to the shipper or a pressure within the shipper.
5. The failure prediction system of claim 1, wherein the processor is further configured to estimate or predict the probability or the likelihood that the shipper will fail before or during a subsequent shipment of the shipper using a machine learning algorithm.
6. The failure prediction system of claim 5, wherein the machine learning algorithm is a boosted decision tree algorithm.
7. The failure prediction system of claim 1, wherein to estimate or predict the probability or the likelihood that the shipper will fail before or during the subsequent shipment of the shipper the processor is configured to estimate or predict a probability or a likelihood that a dynamic holding time of the shipper is less than a threshold amount.
8. The failure prediction system of claim 1, further comprising: a second sensor configured to measure or detect second sensor data, wherein the first sensor is a temperature sensor and the first sensor data is a temperature within the shipper and the second sensor is a pressure sensor and the second sensor data is a pressure within the shipper.
9. The failure prediction system of claim 1, wherein the processor is configured to: obtain user input that indicates a type, model or identifier of the shipper; obtain maintenance information related to the type, model or the identifier of the shipper; and estimate or predict the probability or the likelihood that the shipper will fail before or during the subsequent shipment of the shipper further based on the maintenance information and the user input.
10. The failure prediction system of claim 9, wherein the maintenance information includes a number of thermal or temperature cycles that the shipper has undergone.
11. A failure prediction system, comprising: a processor configured to: obtain at least one of maintenance information or sensor data, estimate or predict a probability or a likelihood that a shipper will fail before or during a subsequent shipment of the shipper based on the at least one of the maintenance information or the sensor data and using a machine learning algorithm, and provide to a user the estimated probability or likelihood that the shipper will fail before or during the subsequent shipment of the shipper; and a display configured to output to the user the estimated probability or likelihood that the shipper will fail before or during the subsequent shipment of the shipper.
12. The failure prediction system of claim 11, further comprising: a memory configured to store a dewar failure model; wherein the processor is configured to estimate or predict the probability or the likelihood that the shipper will fail before or during the subsequent shipment of the shipper further based on the dewar failure model.
13. The failure prediction system of claim 12, further comprising: a user interface configured receive user input that indicates whether the shipper failed before during the subsequent shipment of the shipper; wherein the processor is configured to: update the dewar failure model based on the user input in real-time and the at least one of the maintenance information or the sensor data in real-time.
14. The failure prediction system of claim 11, further comprising: a sensor configured to measure or detect the sensor data, wherein the sensor includes at least one of a temperature sensor, a shock or vibration sensor, or a pressure sensor and the sensor data includes at least one of a temperature within the shipper, shocks or vibrations to the shipper or a pressure within the shipper.
15. The failure prediction system of claim 14, wherein the processor is further configured to estimate or predict the probability or the likelihood that the shipper will fail before or during a subsequent shipment of the shipper using a machine learning algorithm.
16. The failure prediction system of claim 11, wherein the machine learning algorithm is a boosted decision tree algorithm.
17. The failure prediction system of claim 11 , wherein to estimate or predict the probability or the likelihood that the shipper will fail before or during the subsequent shipment of the shipper the processor is configured to estimate or predict a probability or a likelihood that a dynamic holding time of the shipper is less than a threshold amount.
18. The failure prediction system of claim 11 , wherein the processor is configured to: obtain user input that indicates a type, model or identifier of the shipper; and estimate or predict the probability or the likelihood that the shipper will fail before or during the subsequent shipment of the shipper further based on the user input.
19. The failure prediction system of claim 11, wherein the maintenance information includes a number of thermal or temperature cycles that the shipper has undergone.
20. A method for predicting failure of a shipper, comprising: obtaining, by a processor, a dewar failure model that models a failure of various shippers; detecting or measuring, by a sensor, sensor data that relates to a failure of the shipper; estimating or predicting, by the processor, a probability or a likelihood that the shipper will fail before or during a subsequent shipment of the shipper based on the sensor data and the dewar failure model; and displaying, by the processor and on a display, the estimated probability or likelihood that the shipper will fail before or during the subsequent shipment of the shipper.
GB2403123.9A 2021-09-16 2022-09-14 Prediction of dewar failure Pending GB2624810A (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US17/477,381 US20230082374A1 (en) 2021-09-16 2021-09-16 Prediction of dewar failure
PCT/US2022/043431 WO2023043774A1 (en) 2021-09-16 2022-09-14 Prediction of dewar failure

Publications (2)

Publication Number Publication Date
GB202403123D0 GB202403123D0 (en) 2024-04-17
GB2624810A true GB2624810A (en) 2024-05-29

Family

ID=90941570

Family Applications (1)

Application Number Title Priority Date Filing Date
GB2403123.9A Pending GB2624810A (en) 2021-09-16 2022-09-14 Prediction of dewar failure

Country Status (2)

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EP (1) EP4381446A1 (en)
GB (1) GB2624810A (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20140015045A (en) * 2012-07-27 2014-02-06 한국철도기술연구원 Management method of dangerous article transport car and management apparatus of dangerous article transport car using the method
WO2017000014A1 (en) * 2015-07-01 2017-01-05 Fixingbits Pty Ltd Improved delivery systems and methods
JP2020135483A (en) * 2019-02-20 2020-08-31 慎平 大杉 Artificial intelligence system, artificial intelligence device, display control device, artificial intelligence system control method, information processing method, artificial intelligence program, and program
US20210158284A1 (en) * 2018-12-20 2021-05-27 Carrier Corporation Interactive system for planning future shipments
KR20210074258A (en) * 2019-08-26 2021-06-21 쿠팡 주식회사 Computerized systems and methods for facilitating package redelivery

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20140015045A (en) * 2012-07-27 2014-02-06 한국철도기술연구원 Management method of dangerous article transport car and management apparatus of dangerous article transport car using the method
WO2017000014A1 (en) * 2015-07-01 2017-01-05 Fixingbits Pty Ltd Improved delivery systems and methods
US20210158284A1 (en) * 2018-12-20 2021-05-27 Carrier Corporation Interactive system for planning future shipments
JP2020135483A (en) * 2019-02-20 2020-08-31 慎平 大杉 Artificial intelligence system, artificial intelligence device, display control device, artificial intelligence system control method, information processing method, artificial intelligence program, and program
KR20210074258A (en) * 2019-08-26 2021-06-21 쿠팡 주식회사 Computerized systems and methods for facilitating package redelivery

Also Published As

Publication number Publication date
GB202403123D0 (en) 2024-04-17
EP4381446A1 (en) 2024-06-12

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