二区SCI期刊Journal of Manufacturing Systems征稿
Special Issue on Prognostic and Health Management through Collaborative Maintenance
Maintenance approach has significantly changed over the last century, with a shift in emphasis from technological to techno-economic considerations. Today, maintenance strategies are applied in an integrated way depending on the criticality of assets and cost-effectiveness. These strategies include but are not limited to the following: corrective maintenance, preventive maintenance, predictive maintenance, proactive and passive maintenance, self-maintenance, smart maintenance, e-Maintenance and collaborative maintenance. The development of these strategies is ongoing, and new requirements are continually created with the increasing complexity of assets and operating conditions.
In Industry 4.0, improved automation technology, including self-awareness and self-learning, is a must. Assets can already “talk” to each other or “learn” by themselves using the Internet of Things (IoT) frameworks, cyber-physical systems (CPSs), machine to machine communications, and industrial Internet, that is, the integration of complex physical machinery with networked sensors and software in a collaborative community. However, the knowledge of intelligent data analysis and the ability to create asset self-awareness and self-learning remain limited and many data are underutilized. These obstacles to the collaborative efficiency of assets limit moving closer to the ultimate goal of intelligent maintenance.
Prognostic and Health Management (PHM) thorough the collaborative community will be needed for more effective and efficient maintenance, lower energy use and increase capacity, and more reliable and robust. Recently, there are several new trends in successfully development and implementation of such PHM programs: First, there is a spoken need of convergence of the Operational Technology (OT), Information Technology (IT) together with Engineering Technologies (ET); Second, purely data driven or physical model-driven approaches may not satisfy the situations of assets in reality; instead, the context-driven approaches are more and more acceptable, which will effectively use all relevant information (including on-board data, test data, equipment mechanisms, context data, etc.) for diagnosis, prognostics, and decision-making; Third, PHM programs are developed and implemented from component level to system level, and to System of system (SoS) level by using Product Lifetime Management (PLM) perspective.
This special session solicits papers that present various PHM program through Collaborative Maintenance, with special focuses on: 1) technology that enables convergence of OT, IT and ET; 2) data-driven, model-based, hybrid-driven, as well as context-driven approaches; 3) studies cover from component to SoS level using PLM. Among them, techniques and approaches used, results obtained, and lessons learned can be included to share experience with this session. These include but not limited to:
l Sensor and data collection, data quality management, data processing, data fusion technologies for PHM;
l Taxonomy and FMECA analytics for PHM;
l Feature extraction methods for PHM;
l Diagnosis, incl., context definition and awareness, condition monitoring, anomaly detection;
l Prognostic, incl., severity analysis, prognostics accuracy analysis, prognostics uncertainty / prognostics horizon analysis;
l Advanced RAM4S approaches for PHM: Reliability, availability, maintainability, safety, security, supportability, sustainability;
l Nowcasting, forecasting and Remaining Useful life (RUL) prediction;
l Maintenance analytics and maintenance decision optimization;
l Maintenance digitalization, visualization, and servitization solutions for PHM applications.
Janet (Jing) Lin, Luleå University of Technology, Sweden, firstname.lastname@example.org
Baoping Cai, China University of Petroleum, China, email@example.com
Submission deadline: 30th Sep, 2020
Acceptance notification: 20th Dec, 2020
Expected publication: 28th Feb, 2021