ICST 2016 solicits special session proposals. Prospective organizers are invited to submit their proposals to the Special Sessions Co-Chairs, Xuefeng Chen (firstname.lastname@example.org) and/or Jinxing Liang (email@example.com), by March 1st, 2016. The special session proposal should include the title of the special session, a short presentation and motivation of the significance of the special session topic.
The special sessions are intended to stimulate in-depth discussions in special areas relevant to the conference theme. Once received, each proposal will be carefully evaluated, and the accepted special sessions will be announced on the conference website.
The organizers are welcome to promote their special sessions through various venues, andwill coordinate the review process for their session papers. The conference proceedings will include all papers from the special sessions.
Special Session 1：Sensor signal processing for machinery fault diagnosis and prognosis
Machinery fault diagnosis and prognosis have drawn increasing attention during the last decade and significant research efforts have been made by both academia and industry. A number of measurement modalities are involved for such purposes, in which, the acoustic or vibration signals collected by sensors have received a broad range of applications as they are information-rich about the machinery health status and its functional degradation trend. To achieve the diagnosis and prognosis, the main challenging is to measure such signals and then develop effective signal processing approaches for machinery fault detection, reliability evaluation, and remaining useful life (RUL) prediction, etc. This special session is orgnaized to report recent advances in the theory and methodology for sensor signal processing to deal with issues encountered in machinery fault diagnosis and prognosis.
Suitable topics for this special session include but are not limited to:
•Data acquisition techniques for machinery
•Advanced time-frequency/scale signal processing
•Facility condition monitoring and machinery fault diagnosis
•Patten recognition based methods for fault diagnosis and prognosis
•Machinery reliability theory and application
•Sensor data based remaining useful life prediction
•Facility health management
•Other related topics.
Prof. Zhongkui Zhu, Soochow University, Email: firstname.lastname@example.org