Data-Driven Decision-Making

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Dimitris Apostolou	+ 1291 word(s)	1291	2021-04-13 06:27:42	\|
2	format correct	Catherine Yang	Meta information modification	1291	2021-04-22 08:40:21	\|

This entry is adapted from the peer-reviewed paper 10.3390/electronics10070828

Decision-making for manufacturing and maintenance operations is benefiting from the advanced sensor infrastructure of Industry 4.0, enabling the use of algorithms that analyze data, predict emerging situations, and recommend mitigating actions.

Internet of Things intelligent decision-making

1. Introduction

The current trend of automation and data exchange in manufacturing is enabled by emerging technological advancements including the Internet of Things (IoT), cloud computing, and cyber-physical systems. This trend is often cited as “Industry 4.0”, “smart manufacturing”, and “digital factory” ^[1]. The large volume of data generated by manufacturing automation and sophisticated machines and sensors have been described in various reviews of industrial communication and data management systems, e.g., ^[2]^[3]. Predictive maintenance in particular is gaining a crucial role in cost reduction and business performance improvement ^[4]. Predictive maintenance utilizes heterogeneous data sources for detecting abnormal behaviors of equipment (diagnosis), predicting future failure modes (prognosis), and supporting decisions ahead of time (proactive decision-making) ^[5].

2. The Role of Decision-Making in Manufacturing Maintenance

Methods used for predictive maintenance can be classified into three categories ^[6]: (i) model-based, relying on the physical models of the equipment operation and the manufacturing process; (ii) knowledge-based, relying on expert knowledge and being addressed by knowledge management systems; and (iii) data-driven, relying on data analytics and machine learning algorithms. In this work, we focus on the data-driven methods for maintenance decision-making.

Condition monitoring, i.e., the process of monitoring the condition in order to identify a significant change that is indicative of a developing fault ^[7], is a major component of predictive maintenance ^[8]. During the last years, due to the emergence of Industry 4.0, condition monitoring techniques have evolved from visual inspections and manual analysis of datasets to high-frequency sensors generating real-time big data on several parameters such as vibration, temperature, and thermography. On the basis of these data, one can apply advanced data analytics techniques in order to handle the uncertainty due to the stochastic degradation process as well as the uncertainty in prognostic output and to support decision-making under time constraints. Decision-making in predictive maintenance indicates the phase that is triggered by data-driven, (near) real-time predictions (e.g., about future failure modes) in order to generate proactive recommendations about maintenance actions and plans that eliminate or mitigate the impact of the predicted failure.

An important and well-established principle of maintenance is the P-F curve, which is shown in Figure 1. The P-F curve indicates how a part of equipment starts being degraded to the point at which the forthcoming failure can be predicted (the potential failure point “P”). Thereafter, if it is not predicted and no suitable action is taken, it continues to deteriorate—usually at an accelerating rate—until it reaches the point of functional failure (Point “F”)—this is known as the P-F interval ^[9]. The P-F interval allows for actions to be taken so as to avoid the forthcoming failure or provide the necessary remedies ^[6].

Figure 1. Potential Failure and Functional Failure (P-F) curve.

3. Data-Driven Decision-Making for Industry 4.0 Maintenance Applications

3.1. Internet of Things

Internet of Things provides a dynamic global network infrastructure with self-configuring capabilities based on standard and interoperable communication protocols where physical and virtual “things” are interconnected and integrated into the information network ^[10]. In the manufacturing context, the value chain should be intelligent, agile, and networked by integrating physical objects, human factors, intelligent machines, smart sensors, the production process, and production lines together ^[11]. Since more and more physical objects are connected to the manufacturing network and high-speed transactional data and information is generated ^[12], scalability is a major challenge in Industry 4.0.

The evolutionary process will lead to networked manufacturing systems with a high degree of autonomy as well as self-optimization capabilities ^[13]. They will be organized in a decentralized manner, increasing robustness and adaptability ^[13]. Therefore, the increasing availability of sensors and actuators will result in the need for decision-making algorithms capable of supporting the autonomy of networked manufacturing systems. On the other hand, the uncertainty existing in decision-making algorithms for maintenance increases the risk of implementing inappropriate autonomous maintenance actions. To this end, methods and techniques for eliminating the uncertainty and for fast learning from the shop-floor are of utmost importance.

3.2. Cloud Computing

The use of cloud-based architectures and technologies is strongly related to effective systems integration, e.g., with the use of RESTful Application Programming Interfaces (APIs) for accessing services provided by cloud computing vendors. Cloud consumers use APIs as software interfaces to connect and consume resources in various ways, although the optimal or contemporary route is to use a RESTful protocol-based API. To this end, the services implementing data-driven decision-making functionalities should allow seamless and modular communication through cloud-based platforms. This direction needs to be developed in alignment with the concept of cloud manufacturing.

Cloud manufacturing is a smart networked manufacturing model that incorporates cloud computing, aiming at meeting the growing demands for broader global cooperation, knowledge-intensive innovation, and increased market-response agility ^[14]. Apart from the technological perspective, this will lead to decision-making algorithms facilitating their implementation in a cloud-based computational environment, but also to domain-specific communication protocols and standards for guiding the development of future algorithms requiring high computational power.

On the other hand, this research direction requires addressing the challenges of reliability, availability, adaptability, and safety on machines and processes across spatial boundaries and disparate data sources ^[15]. In addition, it needs to tackle the privacy and security aspects on the cloud. Therefore, there is the need for robust algorithms that can accurately support decision-making in the presence of uncertainty as well as methods to quantify their confidence in a real-time and computationally demanding environment.

3.3. Big Data Analytics

The collection and processing of data from many different sources have significantly enabled the information that is available to engineers and operators in manufacturing facilities. Data management and distribution in the big data environment is critical for achieving self-aware and self-learning machines and for supporting manufacturing decisions. Data analytics is categorized into three main stages characterized by different levels of difficulty, value, and intelligence ^[16]: (i) descriptive analytics, answering the questions “What has happened?”, “Why did it happen?”, but also “What is happening now?”; (ii) predictive analytics, answering the questions “What will happen?” and “Why will it happen?” in the future; and (iii) prescriptive analytics, answering the questions “What should I do?” and “Why should I do it?”.

Although big data analytics has been extensively used for real-time diagnosis and prognosis in the context of predictive maintenance, their utilization in decision-making algorithms is still at its early stages. Since the research interest has been gathered to descriptive and predictive analytics ^[17], the immaturity of prescriptive analytics has inevitably affected the predictive maintenance decision-making algorithms as well. As we presented in our literature review, the vast majority of the existing predictive maintenance decision-making algorithms rely on traditional mathematical programming methods. On the other hand, prescriptive analytics has been realized mainly with domain-specific expert systems or optimization models ^[18].

However, there is the need for data-driven generic decision-making algorithms representing the decision-making process instead of the physical process. Building physical models of industrial assets and processes is a complex and laborious task that does not necessary exploit the knowledge that can be discovered from data, such as failure patterns or patterns of causes and effects. Moreover, knowledge-based decision-making methods are rather static, not capable of self-adaptation on the basis of emerging data. However, dynamic shop-floor conditions require real-time decision-making, which requires both advanced data infrastructures, e.g., distributed cloud computing for processing and storing data, as well as new functionalities, e.g., computationally efficient, probabilistic algorithms tailored for streaming data and capable of handling uncertainty. Finally, feedback mechanisms should be employed in order to adapt decision-making on the basis of changing conditions such as new operating constraints. Although feedback mechanisms for diagnostic and prognostic algorithms have been well perceived, mechanisms for tracking the recommended actions are still few in number, e.g., ^[19].

References

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