Research Papers

Online Analytics Framework of Sensor-Driven Prognosis and Opportunistic Maintenance for Mass Customization

[+] Author and Article Information
Tangbin Xia

State Key Laboratory of Mechanical System and Vibration,
Department of Industrial Engineering,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: xtbxtb@sjtu.edu.cn

Xiaolei Fang

Edward P. Fitts Department of Industrial and Systems Engineering,
North Carolina State University,
111 Lampe Drive, Raleigh, NC 27607
e-mail: xfang8@ncsu.edu

Nagi Gebraeel

H. Milton Stewart School of Industrial and Systems Engineering,
Georgia Institute of Technology,
765 Ferst Drive, Atlanta, GA 30332
e-mail: nagi.gebraeel@isye.gatech.edu

Lifeng Xi

State Key Laboratory of Mechanical System and Vibration,
Department of Industrial Engineering, School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: lfxi@sjtu.edu.cn

Ershun Pan

State Key Laboratory of Mechanical System and Vibration,
Department of Industrial Engineering,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: pes@sjtu.edu.cn

1Corresponding author.

Manuscript received September 10, 2018; final manuscript received March 14, 2019; published online April 2, 2019. Assoc. Editor: Qiang Huang.

J. Manuf. Sci. Eng 141(5), 051011 (Apr 02, 2019) (12 pages) Paper No: MANU-18-1648; doi: 10.1115/1.4043255 History: Received September 10, 2018; Accepted March 14, 2019

In mass customization, a manufacturing line is required to be kept in reliable operation to handle product demand volatility and potential machine degradations. Recent advances in data acquisition and processing allow for effective maintenance scheduling. This paper presents a systematic framework that integrates a sensor-driven prognostic method and an opportunistic maintenance policy. The prognostic method uses degradation signals of each individual machine to predict and update its time-to-failure (TTF) distributions in real time. Then, system-level opportunistic maintenance optimizations are dynamically made according to real-time TTF distributions and variable product orders. The online analytics framework is demonstrated through the case study based on the collected reliability information from a production line of engine crankshaft. The results can effectively prove that the real-time degradation updating and the opportunistic maintenance scheduling can efficiently reduce maintenance cost, avoid system breakdown, and ensure product quality. Furthermore, this framework can be applied not only in an automobile line but also for a broader range of manufacturing lines in mass customization.

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Grahic Jump Location
Fig. 1

PM adjustment optimization of BMA policy

Grahic Jump Location
Fig. 3

Degradation signals of individual machines: (a) degradation signals (M1), (b) degradation signals (M2), and (c) degradation signals (M3)

Grahic Jump Location
Fig. 4

Production information of sequential orders

Grahic Jump Location
Fig. 2

Illustration of CRM-BMA methodology

Grahic Jump Location
Fig. 5

Real-time updating of machines 1, 2, and 3 at t = 1000: (a) time-to-failure density (machine 1), (b) hazard function (machine 1), (c) time-to-failure density (machine 2), (d) hazard function (machine 2), (e) time-to-failure density (machine 3), and (f) hazard function (machine 3)



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