Design for diagnosability of multistation manufacturing systems based on sensor allocation optimization
Introduction
Manufacturing enterprises are facing unpredictable and rapidly changing market competition driven by customer demands. To survive and remain competitive in such an environment, tremendous efforts have been taken to improve product quality. These quality issues have been manifested in advanced manufacturing processes that require frequent reconfigurations to produce different products in various quantities, while keeping high efficiency. Tool condition monitoring has gained considerable importance in the manufacturing industry over the preceding two decades, as it significantly influences process efficiency and machined part quality [1], [2]. Meanwhile, dimensional variation causes quality defects, which are a major cause of faults in manufacturing systems [3]. Consequently, it is necessary to monitor the key product characteristics (KPCs) in such a system to reach a desired quality level [4]. Quality monitoring is the prerequisite for fault diagnosis, which is then performed to identify faults caused by the dimensional variations and root causes based on the states of the KPCs [5]; all these are manifested by abundant sensing data. It is of high priority to ensure that the KPCs are optimally selected, and are measured by sensors in a cost-effective, timely manner.
Measurement scheme and sensor allocation optimization are essential in the early design phase of a multistation manufacturing system (MMS), which utilizes plenty of sensors for multivariate measuring both in-line and off-line. A huge amount of sensing data is helpful for system diagnostic functionality enhancement. However, sensing data acquisition without proper sensor allocation is inapplicable. It has a great significance on some other factors, such as cost, energy consumption, space, and efficacy of the measurement system. Effective use of sensing data for diagnosis depends to a great extent on the optimal design of a measurement system. Therefore, the concept of diagnosability is introduced to assess the degree to which the quality-related faults in a manufacturing system is diagnosable. A design for diagnosability (DFD) structure should be formulated and implemented during the design phase as early as possible. The KPCs must be continuously monitored and measured to ensure that any quality-related faults owing to dimensional variation or other abnormal conditions can be promptly detected and diagnosed. Conversely, an inadequately designed measurement system is apt to generate some irrelevant or even conflicting information, which is incapable of offering preferred diagnosability to identify faults and further root cause the dimensional variation sources.
Research in the fields of sensor allocation and system diagnosability are limited due to the early stage of development. However, several researchers have conceptually investigated, and elaborated on viable approaches, to solve these problems. Sensor allocation optimization and system diagnosability have been addressed in recent literature [6], and they have been applied in the design of manufacturing systems [7], [8], [9]. Liu et al. [10] have proposed an axiomatic design methodology for a manufacturing system based on a diagnosability matrix. Xia and Rao [11] have presented a design approach for sensor and actuator placement in the process industry. Ding et al. [12] have investigated global and local diagnosability due to between-station and across-station variation propagation, and further pointed out that system diagnosability could be calculated based on the rank of the diagnosis matrix. Many effective optimization approaches exist for sensor allocation to determine sufficient information for dynamic analysis, such as the covariance matrix approach [13], [14], the eigensystem realization algorithm [15], the Bayesian approach [16], the effective independence approach [17], the systematic procedure [18], the maximum energy approach [19] as well as the genetic algorithm [20]. However, these approaches, besides being highly computationally intensive, suffer from a major drawback of yielding hardly any insight into why certain sensor locations are preferable to others. Furthermore, recent sensor optimization research has mainly been restricted to single station, and little can be found about the system level or the multistation environment. Yan [21] has developed a method for sensor installation based on the diagnosability analysis. A simulation model in a CAD environment has been developed and the minimal sensor set from a fault signature matrix has been computed. Fisher information matrix has been frequently used in optimization for a single station [22]. Khan and Ceglarek [23] have formalized an approach to find optimal sensor allocation by maximizing the minimum distance between two variation vectors. However, it has failed to consider the variation propagation between various stations. Therefore, a new approach for sensor allocation should be developed for the applications frequently encountered in industrial practice.
As is seen from the literature, no research has yet combined the design for diagnosability methodology of a multistation manufacturing system with the sensor allocation optimization. This research focuses on the impacts of distinctive measurement schemes and the associated sensor allocation strategies. The remainder of the paper is organized as follows: Section 2 provides a methodology for the diagnosability analysis, and three indices are proposed for the design for diagnosability. Section 3 formulates the optimization procedures of the sensor allocation for minimizing the sensing cost and reducing the time to diagnosis without any loss of diagnosability. An industrial example is used to illustrate the application of the proposed approach in Section 4. Finally, Section 5 gives some concluding remarks.
Section snippets
Design for diagnosability
Fault diagnosis is an indirect process initiated from the dimensional variation measurement of the KPCs, through information collection and processing, signature extraction, to identification of the quality status. Thanks to the advances in measuring technologies, sensing data are collected not only for quality assurance and process monitoring, but also for fault diagnosis of quality-related problems. In this fashion, sensing data is used to diagnose the underlying sources that cause the
Variation transmission and diagnosis
The objective of optimal sensor allocation is to achieve the desired diagnosability with the minimum cost and the time to diagnosis. The cost of a measurement system comes not only from installing individual sensors, but also from the expense of introducing additional sensing stations at specific positions in the system. It is assumed that all parts manufactured on any upstream stations can be fully measured and accessed by the sensors on a downstream station. Thus, there are two different ways
Case study
An illustrative example is performed to validate the importance and effectiveness of the proposed analytical procedure. The machining process of a box-type workpiece in this case study is a complex one, and several operations are involved in which a number of distinctive features are machined. The three dimension schematic illustration of the final box-type product is illustrated in Fig. 3. The operations include forging the roughcast, rough milling, fine milling, scribing, drilling and
Conclusions
A key issue for practical diagnosis in industry nowadays is the trade-off of sensor reduction, while obtaining a high degree of system diagnosability and better dimensional quality. A theoretical formulation for sensor allocation optimization is proposed for a multistation manufacturing system. Unique properties of dimensional variation transmission and diagnosis are considered. A heuristic backward propagation algorithm determines an optimal sensor allocation strategy, which includes the
Acknowledgements
The authors greatly acknowledge the editor and the referees for their valuable comments and suggestions that have led to a substantial improvement of the paper. The authors also thank financial support from the Natural Science Foundation of China (NSFC) with grant numbers 50675137 and 70671065, and the Program of Introducing Talents of Discipline to Universities with the grant number B06012.
Mr. Jiwen Sun received his BS and MS degrees in Mechanical Engineering from Hefei University of Technology, Hefei, PR China, in 2002 and 2005 respectively. He is currently working towards his Ph.D. degree at the Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, PR China. He has published 8 papers. His research interests are centered on sensing system optimization, quality control system.
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Mr. Jiwen Sun received his BS and MS degrees in Mechanical Engineering from Hefei University of Technology, Hefei, PR China, in 2002 and 2005 respectively. He is currently working towards his Ph.D. degree at the Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, PR China. He has published 8 papers. His research interests are centered on sensing system optimization, quality control system.
Dr. Prof. Lifeng Xi received his Ph.D. degree in Mechanical Engineering in 1995 from Shanghai Jiao Tong University, Shanghai, PR China. He is currently a professor at the School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, PR China. His 60 papers and 2 books have been accepted or published. His research interests include production system modeling, quality control, sensing system optimization.
Dr. Prof. Ershun Pan received his Ph.D. degree in Mechanical Engineering in 2002 from Shanghai Jiao Tong University, Shanghai, PR China. He is currently an associate professor at the School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, PR China. His 17 papers and 1 book have been published. His research interests include quality management and control technology, automatic process control and system optimization.
Dr. Shichang Du received his Ph.D. degrees in Mechanical Engineering from Shanghai Jiao Tong University, Shanghai, PR China in 2008. He is currently conducting the Post Doctor research at the same university. His research interests are centered on product lifecycle management, quality and productivity improvement methodologies for complex manufacturing systems.
Mr. Tangbin Xia received his BS degree in Mechanical Engineering in 2007 from Shanghai Jiao Tong University, Shanghai, PR China. He is currently working towards his Ph.D. degree at the School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, PR China. He has published 2 papers. His research interests include quality and reliability modeling, automatic process control and system optimization.