18108048. METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR PROCESSING FAULTS simplified abstract (Dell Products L.P.)

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METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR PROCESSING FAULTS

Organization Name

Dell Products L.P.

Inventor(s)

Jiacheng Ni of Shanghai (CN)

Zijia Wang of Weifang (CN)

Jinpeng Liu of Shanghai (CN)

METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR PROCESSING FAULTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18108048 titled 'METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR PROCESSING FAULTS

The abstract of this patent application describes a method involving multiple deep learning models to diagnose and address faults in a storage device.

  • The first deep learning model determines if a fault is caused by an environmental factor.
  • The second deep learning model assesses if the fault can be fixed locally within the storage device.
  • If the fault can be solved locally, it is processed according to a knowledge base.
  • If the fault cannot be fixed locally, it is sent to a third deep learning model on a cloud device.
  • The first and second deep learning models are derived from the third deep learning model through model distillation.

Potential Applications: - Automated fault diagnosis and resolution in storage devices. - Enhanced maintenance and troubleshooting processes in data centers. - Improved reliability and performance of storage systems.

Problems Solved: - Efficient identification of fault causes. - Streamlined fault resolution procedures. - Reduction of downtime and maintenance costs.

Benefits: - Faster and more accurate fault diagnosis. - Minimized disruptions to storage operations. - Enhanced overall system reliability.

Commercial Applications: Title: "Advanced Fault Diagnosis System for Storage Devices" This technology can be utilized by data centers, cloud service providers, and IT companies to optimize storage device maintenance, improve system performance, and reduce operational costs.

Questions about the technology: 1. How does this method compare to traditional fault diagnosis approaches in terms of accuracy and efficiency? 2. What are the potential limitations or challenges of implementing multiple deep learning models for fault diagnosis in storage devices?

Frequently Updated Research: Stay updated on advancements in deep learning models for fault diagnosis in storage systems, as well as developments in cloud-based fault resolution technologies.


Original Abstract Submitted

A method in an illustrative embodiment includes determining, by a first deep learning model on a storage device, whether a fault is caused by an environmental factor. The method further comprises: determining, by a second deep learning model on the storage device in response to determining that the fault is caused by the environmental factor, whether the fault can be solved locally in the storage device. The method further comprises: processing the fault according to a knowledge base in response to determining that the fault can be solved locally in the storage device. The method further comprises: sending the fault to a third deep learning model on a cloud device in response to determining that the fault cannot be solved locally in the storage device, the first deep learning model and the second deep learning model being obtained by model distillation of the third deep learning model.

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