Aircraft Predictive Maintenance and Workflow Optimization
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.
SparkCognitionSparkCognition is a global leader in cognitive computing analytics. A highly awarded company recognized for cutting-edge technology, SparkCognition develops AI-Powered cyber-physical software for the safety, security, and reliability of IT, OT, and the IoT.The company's technology is capable of harnessing real-time sensor data and learning from it continuously, allowing for more accurate risk mitigation and prevention policies to intervene and avert disasters. SparkCognition???s cognitive software, DeepArmor, is utilized to prevent and detect Malware with a high level of accuracy and efficacy by using advanced Machine Learning and Artificial Intelligence.
- CONNECTIVITY PROTOCOLS
- USE CASES
Predictive MaintenancePredictive maintenance is a technique that uses condition-monitoring sensors and Machine Learning or rules based algorithms to track the performance of equipment during normal operation and detect possible defects before they result in failure. Predictive Maintenance enables the reduction of both schedule-based maintenance and unplanned reactive maintenance by triggering maintenance calls based on the actual status of the equipment. IoT relies on Predictive Maintenance sensors to capture information, make sense of it, and identify any areas that need attention. Some examples of using Predictive Maintenance and Predictive Maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. Visit our condition-based maintenance page to learn more about these methods.
An aircraft manufacturer.
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
- SOLUTION TYPE
- SOLUTION MATURITY
Emerging (technology has been on the market for > 2 years)
- OPERATIONAL IMPACT
Impact #1 [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.
Impact #2 [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.
- QUANTITATIVE BENEFIT