18681589. MAINTENANCE RESPONSE TIME PROPOSAL APPARATUS, METHOD AND PROGRAM simplified abstract (NIPPON TELEGRAPH AND TELEPHONE CORPORATION)
Contents
MAINTENANCE RESPONSE TIME PROPOSAL APPARATUS, METHOD AND PROGRAM
Organization Name
NIPPON TELEGRAPH AND TELEPHONE CORPORATION
Inventor(s)
Yuichiro Ishizuka of Musashino-shi, Tokyo (JP)
Kei Takeshita of Musashino-shi, Tokyo (JP)
Yuji Soejima of Musashino-shi, Tokyo (JP)
MAINTENANCE RESPONSE TIME PROPOSAL APPARATUS, METHOD AND PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18681589 titled 'MAINTENANCE RESPONSE TIME PROPOSAL APPARATUS, METHOD AND PROGRAM
Simplified Explanation:
This patent application describes a device that suggests a response time for preventive maintenance against device malfunction. The device includes units for receiving sign detection information, resource utilization plan information, and estimating transition data of resource usage based on machine learning.
- The device includes a sign receiving unit for detecting signs of device malfunction.
- It also has a resource receiving unit for receiving information on resource utilization plans related to the predicted occurrence period of the malfunction.
- A resource estimation unit calculates transition data of resource usage based on input from the resource utilization plan and machine learning.
- A countermeasure determination unit suggests a time and method for taking countermeasures against the malfunction based on the sign detection information and resource usage data.
Key Features and Innovation:
- Sign detection unit for identifying signs of device malfunction.
- Resource utilization plan unit for planning resource usage related to predicted malfunction periods.
- Resource estimation unit using machine learning to calculate resource usage transition data.
- Countermeasure determination unit for suggesting maintenance response times based on sign detection and resource usage data.
Potential Applications:
This technology can be applied in various industries where preventive maintenance is crucial to avoid device malfunctions and downtime.
Problems Solved:
This technology addresses the challenge of determining the optimal response time for preventive maintenance based on signs of device malfunction and resource utilization plans.
Benefits:
- Improved maintenance efficiency.
- Reduced downtime due to proactive maintenance.
- Cost savings by preventing major malfunctions.
Commercial Applications:
This technology can be used in manufacturing plants, healthcare facilities, and other industries where equipment maintenance is critical for operations.
Questions about the Technology:
1. How does the device determine the optimal response time for preventive maintenance? 2. What are the key advantages of using machine learning for resource estimation in this context?
Frequently Updated Research:
Stay updated on advancements in machine learning algorithms for resource estimation and predictive maintenance strategies.
Original Abstract Submitted
A maintenance response time suggestion device suggests a response time for preventive maintenance against malfunction of a device. The device includes: a sign receiving unit that receives sign detection information in which a sign of malfunction of the device is detected; a resource receiving unit that receives resource utilization plan information that indicates a plan for a usage and a utilization time of human and physical resources related to a predicted occurrence period of the malfunction of the device; a resource estimation unit that estimates and calculates transition data of a usage amount of the human and physical resources related to the predicted occurrence period of the malfunction of the device by inputting the usage and the utilization time of the human and physical resources included in the resource utilization plan information to a machine learning engine that generates transition data of a usage amount of human and physical resources based on a usage and a utilization period of human and physical resources and performing machine learning; and a countermeasure determination unit that determines a time and a method for taking countermeasures against the malfunction of the device based on the sign detection information and the transition data of the usage amount of the human and physical resources.
