SparkCognition > Case Studies > Aircraft Predictive Maintenance and Workflow Optimization

Aircraft Predictive Maintenance and Workflow Optimization

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Technology Category
  • Analytics & Modeling - Machine Learning
  • Analytics & Modeling - Predictive Analytics
Applicable Industries
  • Aerospace
Applicable Functions
  • Maintenance
Use Cases
  • Predictive Maintenance
  • Root Cause Analysis & Diagnosis
The Challenge

First, aircraft manufacturer have trouble monitoring the health of aircraft systems with health prognostics and deliver predictive maintenance insights. Second, aircraft manufacturer wants a solution that can provide an in-context advisory and align job assignments to match technician experience and expertise.

About The Customer
An aircraft manufacturer.
The Solution

SparkPredict leverages cutting edge machine learning techniques to build machine-scale pattern recognition models to monitor mechanical systems within an aircraft, and predict failure. The cognitive nature of these algorithms means that SparkPredict can be deployed to an aircraft system in any location and the insights will adapt to the unique characteristics of that particular plane. In addition, SparkPredict can integrate with other systems such as diagnostic databases, maintenance records, and personnel records to help classify fault codes, recommend the right personnel, and schedule maintenance in an optimal manner. This will reduce the time an aircraft has to spend on the ground.

Data Collected
Asset Performance, Asset Status Tracking, Device Diagnostic Status, Fault Detection, Maintenance Records
Operational Impact
  • [Process Optimization - Remote Diagnostics]
    By leveraging data feeds specific to aircraft systems, SparkPredict can accurately deliver health assessment of aircraft mechanical systems and symptom-based early warning of impending failures.
  • [Efficiency Improvement - Labor]
    By making intelligent choices regarding priorities, expertise and schedule constraints SparkPredict can provide assignments based on crew performance / expertise and dynamic reprioritization based on triggers such as critical fault discovery.

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