Health monitoring algorithms for Big Vehicle Data
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 365
Special Issue Editor
Interests: predictive maintenance; heath monitoring for ground and aerial vehicles; data analytics; AI; innovation; nonlinear systems analysis and synthesis; adaptation; estimation; filtering; control; general artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the proliferation of sensors on vehicles, combined with electronic record keeping for maintenance, much vehicle data is available today. Given the large sampling frequencies of many vehicle electronic systems, the size of these sensor and maintenance records is enormous, which definitely qualifies for the moniker Big Vehicle Data. Soon, these vehicles may be interconnected and autonomous, resulting in even greater quantities of data generated and necessitating the development of algorithms that integrate more systems and process larger quantities of data.
This Issue calls for articles addressing all of the issues arising from the challenges of vehicle maintenance and lifecycle management using Big Vehicle Data. Publications are solicited in the following areas:
- The design of sensing, vehicle systems, and maintenance methods to make predictive maintenance and lifecycle management possible;
- Collecting, communicating, securing, and validating Big Vehicle Data;
- Algorithms for vehicle state estimation, fault isolation, diagnostics, and prognostics;
- The integration of this health monitoring with vehicle control, lifecycle management, and logistics;
- The development of financial, insurance, or business models for monetizing Big Vehicle Data.
Dr. Kartik B. Ariyur
Guest Editor
Manuscript Submission Information
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Keywords
- predictive maintenance
- virtual sensors
- condition-based monitoring
- vehicle health monitoring
- prognostics
- diagnostics
- algorithms
- sensors
- virtualization
- reach
- observational
- fault isolation
- lifecycle management
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