The fundamental process of predictive maintenance is prognostics and health management, and it
is the tool resulting in the development of many algorithms to predict the remaining useful life of industrial
equipment. A new data-driven predictive maintenance and an architectural impulse, based on a regularized deep
neural network using predictive analytics, are proposed successfully for ring spinning technology. The paradigm
shift in computational infrastructures enormously puts pressure on large-scale linear and non-linear automated
assembly systems to eliminate and cut down unscheduled downtime and unexpected stoppages. The sensor
network designed for the scheduling process comprises different critical components of the same spinning machine
frames containing more than thousands of spindles attached to them. We established a genetic algorithm based
on multi-sensor performance assessment and prediction procedure for the spinning system. Results show that it
operates with a relatively less amount of training data sets but takes advantage of larger volumes of data. This
integrated system aims to prognosticate abnormalities, disturbances, and failures by providing condition-based
monitoring for each component, which makes it more accurate to locate the defined component failures in the
current spinning spindles by using smart agents during the operations through the neural sensing network. A case
study has provided to demonstrate the feasibility of the proposed predictive model for highly dynamic, high-speed
textile spinning system through real-time data sensing and signal processing via the industrial Internet of Things.
FAROOQ Basit, BAO Jinsong, LI Jie, LIU Tianyuan, YIN Shiyong
. Data-Driven Predictive Maintenance Approach for Spinning Cyber-Physical Production System[J]. Journal of Shanghai Jiaotong University(Science), 2020
, 25(4)
: 453
-462
.
DOI: 10.1007/s12204-020-2178-z
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