Component Failure Prediction Using ML Mappings
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Solution Overview
Problem
Customer care personnel face challenges in identifying faulty components during system failures, especially when external diagnostic utilities are not permitted, leading to incorrect identification and repeat dispatches, causing customer dissatisfaction.
Innovation Solution
A method utilizing machine learning techniques to predict problematic components by generating mappings between operating conditions data, component replacement data, and no fault found (NFF) data, allowing for accurate identification of failed components based on symptoms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If customer care personnel use guided resolution steps to identify faulty components, then the process can be performed without external diagnostic utilities, but the identification accuracy deteriorates leading to incorrect component identification
Solution Approach 1:
The system performs preliminary data collection and processing before diagnosis is needed. Historical data from multiple sources (system logs, sensor data, replacement data, NFF data) is pre-collected and stored, so when a symptom occurs, the matching can be performed immediately without needing external diagnostic utilities during the actual diagnosis moment.
Solution Approach 2:
Instead of using external diagnostic utilities that may not be permitted, the system creates an internal copy of diagnostic capabilities by collecting and analyzing historical data patterns. The system learns from past diagnoses and replacements to create an internal knowledge base that replicates the diagnostic function without requiring external tools.
2Adaptability or versatility
If manual guided resolution steps are used for component identification, then external diagnostic utilities are not required, but the process becomes error-prone and time-consuming
Solution Approach 1:
The system performs self-service by automatically collecting data from multiple internal sources, processing it through machine learning models, and generating component predictions without requiring external diagnostic utilities. The system serves its own diagnostic needs using its internal data infrastructure and analytical capabilities.
Solution Approach 2:
The system incorporates feedback loops where NFF (no fault found) data and actual replacement outcomes are fed back into the training data. This continuous feedback improves the accuracy of predictions over time, allowing the system to learn from past errors and improve diagnostic speed and efficiency iteratively.
3Measurement precision
If external diagnostic utilities are used, then accurate component identification can be achieved, but security restrictions prevent their use in enterprise environments
Solution Approach 1:
The system introduces an intermediary layer between the need for diagnostic accuracy and the security restrictions. Instead of directly using external diagnostic utilities that are blocked by security policies, the system uses internal data sources (system logs, sensor data, historical replacement data) as intermediaries to achieve diagnostic goals without violating security constraints.
Solution Approach 2:
The system replaces the mechanical approach of running external diagnostic utilities with a data-driven software-based approach. Instead of executing external diagnostic programs that require security permissions, the system substitutes this with internal data collection, processing, and machine learning-based prediction that operates within the existing security framework.
4Reliability
If multiple diagnostic utilities are run to identify faulty components, then comprehensive diagnosis can be performed, but the time required for identification increases
Solution Approach 1:
The system merges multiple data sources that would traditionally require separate diagnostic utilities into a single integrated analysis process. By combining system logs, sensor data, historical replacement data, and NFF data into one unified machine learning model, the system achieves comprehensive diagnosis in a single operation rather than requiring multiple sequential diagnostic steps.
Solution Approach 2:
The system performs preliminary data aggregation and processing before the actual diagnosis is needed. Historical data from multiple sources is pre-collected and stored in ready-to-use formats, so when a symptom occurs, the system can immediately perform pattern matching without the time delay of collecting and processing data during the actual diagnosis moment.
Data Source
AI summary
A method comprises retrieving operating conditions data comprising operational details of one or more components in at least one computing environment. Component replacement data and no fault found (NFF) data of the computing environment are also retrieved. The component replacement data comprises details about components that have been replaced in the computing environment. The NFF data comprises details about components incorrectly identified as having failed in the computing environment and symptoms leading to the incorrect identifications. The method also comprises generating a first mapping between given ones of the operational details and given ones of the replaced components, and generating a second mapping between given ones of the incorrectly identified components and given ones of the symptoms using one or more machine learning algorithms. Using the first and second mappings, at least one failed component is predicted based on one or more symptoms identified in a received support case.