Closed-loop design evolution of engineering system using condition monitoring through internet of things and cloud computing

Abstract

Flexibility of a manufacturing system is quite important and advantageous in modern industry, which function in a competitive environment where market diversity and the need for customized product are growing. Key machinery in a manufacturing system should be reliable, flexible, intelligent, less complex, and cost effective. To achieve these goals, the design methodologies for engineering systems should be revisited and improved. In particular, continuous or on-demand design improvements have to be incorporated rapidly and effectively in order to address new design requirements or resolve potential weaknesses of the original design. Design of an engineering system, which is typically a multi-domain system, can become complicated due to its complex structure and possible dynamic coupling between domains. An integrated and concurrent approach should be considered in the design process, in particular in the conceptual and detailed design phases. In the context of multi-domain design, attention has been given recently to such subjects as multi-criteria decision making, multi-domain modeling, evolutionary computing, and genetic programing. More recently, machine condition monitoring has been considered for integration into a scheme of design evolution even though many challenges exist for this to become a reality such as lack of systematic approaches and the existence of technical barriers in massive condition data acquisition, transmission, storage and mining. Recently, the internet of things (IoT) and cloud computing (CC) are being developed quickly and they offer new opportunities for evolutionary design for such tasks as data acquisition, storage and processing. In this paper, a framework for the closed-loop design evolution of engineering systems is proposed in order to achieve continuous design improvement for an engineering system through the use of a machine condition monitoring system assisted by IoT and CC. New design requirements or the detection of design weaknesses of an existing engineering system can be addressed through the proposed framework. A design knowledge base that is constructed by integrating design expertise from domain experts, on-line process information from condition monitoring and other design information from various sources is proposed to realize and supervise the design process so as to achieve increased efficiency, design speed, and effectiveness. The framework developed in this paper is illustrated by using a case study of design evolution of an industrial manufacturing system.

Introduction

Globalization has intensely changed the engineering manufacturing sector as is the case in many other areas. The growing demand for novel, high quality and highly customized products at low cost with rapid adaptation to market diversity is fundamentally changing the way production systems are designed and implemented [1]. With the development of information, communication, management, sensing and other technologies and theories, various advanced manufacturing system methodologies have been proposed such as lean manufacturing, agile manufacturing, flexible manufacturing, concurrent manufacturing, sustainable manufacturing, global manufacturing, and so on, in order to accommodate the current extremely dynamic operating environment of manufacturing companies such as market variations, changes to time and quantity of product demand and manufacturing system failure [2]. Most of this new research and development has contributed to advanced manufacturing system at the level of manufacturing and planning. However, one fundamental and significant element in forming a competitive manufacturing system that can adapt to rapid market changes is the capability of automated and evolutionary reconfiguration and design improvement of the system.

In modern production processes, most engineering systems are in fact multi-domain mechatronic systems [3], [4], which consist of different domains such as mechanical, electrical, hydraulic, pneumatic, thermal, control, and so on, examples of which are automated packaging lines [5] and car assembly lines with industrial robots [6]. The reconfiguration or design improvement of a multi-domain multi-component system will require simultaneous consideration of all its components and characteristics [7]. This particularly means that dynamic interactions between domains should be considered concurrently throughout the design process. Concurrent, multi-criteria and optimal design are the current main challenges in the design of mechatronic systems [8]. Research on the design methodologies of mechatronic systems is becoming active, which aims to achieve a design with increased reliability and flexibility, greater intelligence, and reduced complexity and cost. The design process of a multi-domain engineering system can be complicated due to its complex structure and dynamic coupling (interaction) between domains. Ideally, designing a multi-domain system should be done in an integrated and concurrent manner, where dynamic interactions between domains in the entire system have to be considered simultaneously, throughout the design process [4], [9]. In recent years, researchers have made some progress in the integrated and optimal design of multi-domain systems. Dynamic modeling tools such as Bond Graphs (BG) [4], [10] and Linear Graphs (LG) [4], [9] have been considered for modeling multi-domain systems, which can facilitate the design process. Design optimization can then be achieved by using methods of evolutionary computing, genetic programing in particular. Koza et al. [11] employed GP for the automated design of electrical circuits. The solution space represented in a tree-like structure is explored by GP. Inspired by the work of Koza et al., Seo et al. [12] combined bond graph modeling with GP to explore the design space of a mechatronic system in achieving an optimal design. Wang et al. [13] utilized a similar framework for the automated design of a controller. Design knowledge was also acquired from their framework to supervise the search of the design space. Behbahani [14] and de Silva [15] extended the combined BG–GP approach for nonlinear mechatronic systems. More recently, machine condition monitoring has been integrated into the framework of evolutionary design optimization [16], [17]. It can provide the information of locations of a possible design weakness which may lead to system malfunctions or unsatisfactory system performance. It shows promising potential for precise and continuous design improvement of complex engineering system. However, the progress of implementing machine condition monitoring to engineering system design improvement is still at the beginning as there are still many issues to be solved. Monitoring of a complex engineering system with a large amount of components brings technical challenges and unaffordable cost in sensing, massive data transmission, storage and processing. For instance, acoustic emission sensors are widely used in detecting early stage failure of rotating machine such as bearings and gearboxes. However, use of one acoustic emission sensor at sampling frequency of 1 MHz will create 200–300 GB raw data per hour which is unaffordable by traditional data storage and processing approaches.

The internet of things (IoT) [18] and cloud computing (CC) [19] have brought about new opportunities for sensing, storage and mining of data, online computing, ubiquitous accessibility and affordable cost. Technologies of IoT and CC have been developed and applied at a rapid rate, which have provided new opportunities to address the challenges in achieving more efficient and effective machine condition monitoring for reconfiguration and design improvement of manufacturing systems. In the field of engineering, evolution of an industrial system from its original creative solution to a modern system progresses gradually, with specific contributions from design experts [20]. However, this process takes a comparatively long time and heavily relies on reliable domain expertise. In this paper, a framework for the design evolution of an engineering system with the assistance of IoT and CC is proposed. Through the process of closed loop design evolution, the engineering system can be improved continuously, efficiently, and cost-effectively. The remaining of the paper is organized as follows. Section 2 introduces the related work on engineering system design, machine condition monitoring and IoT and CC. The framework of the closed loop design evolution of an engineering system is described in Section 3. A comprehensive case study is performed in Section 4 to demonstrate the application of the proposed framework for design evolution. Section 5 concludes the paper and indicates possible future work.