An anti-jamming artificial immune approach for energy leakage diagnosis in parallel-machine job shops
Introduction
With the increasing level of complexity and automation in manufacturing processes, manufacturers need more effective and efficient techniques to monitor the operation status and diagnose process faults of machines to enable a sustainable, economical, and environment-friendly production [1], [2], [3]. Energy leakage, such as steam, compressed air, and cool air leakage from broken pipelines or worn valves, is a typical fault in job shops with high-energy consumption. This fault tends to cause enormous economic losses and environmental pollution [4], [5], [6], and hence, its diagnosis becomes an essential requirement for an economical and environment-friendly production.
Energy leakage diagnosis is a combination of fault detection (which identifies if there is an energy leakage) and isolation (which determines the location of the energy leakage) [7]. Owing to the complexity of the industrial environment, energy leakage diagnosis is a very challenging activity. Bayar et al. [1] and Wang et al. [8] pointed out that in order to develop a fault diagnosis system, several limitations (e.g. data acquisition and processing, tolerances and sensitivity to change, and alarm frequency and quality) should be considered. For energy leakage diagnosis in many industrial environments, energy consumption is measured at the shop/group level rather than at the machine level owing to the issues of technology, such as too small branch pipe, too narrow installation space, strong vibration of machine, etc. The limited energy measurements increase the difficulty in isolating the leaking machines. Moreover, stochastic parallel jobs with multi-process and random operation rhythm reduce the sensitivity of energy consumption to energy leakage. Furthermore, the fluctuating energy supply and environmental dynamics confuse the fluctuation of process parameters caused by energy leakage. In particular, for parallel-machine job shops, independent machines and parallel jobs make energy leakage isolation more complicated. Therefore, for parallel-machine job shops, an energy leakage diagnosis approach needs to overcome a jamming environment with limited energy measurements, stochastic parallel jobs, and a fluctuating energy supply.
In recent years, many approaches for energy leakage diagnosis have been presented based on fluid mechanics. These approaches, such as pressure-based diagnosis [9], [10], [11], negative-pressure-wave-based diagnosis [12], electromagnetic-wave-based diagnosis [13], or hybrid techniques [14], focus more on identifying a disruption or mutation of parameters in energy transmission pipelines with regular topology and stable flow, rather than on isolating a potential leaking machine from a jamming industrial environment. These approaches are not suitable for energy leakage diagnosis in parallel-machine job shops because they ignore the fluctuating energy supply, environmental dynamics, and limited data acquisition.
In addition, based on energy consumption mechanisms, various energy consumption models were proposed to diagnose energy leakage. Duflou et al. [15] proposed an organizational energy consumption model including five levels: device/unit process, line/cell/multi-machine system, facility, multi-factory system, and enterprise/global supply chain. Rahimifard et al. [16], Wang et al. [17], and Li et al. [18] proposed productive energy consumption models including three levels: process, product, and production. Bi and Wang [19] and Pfefferkorn et al. [20] suggested technological energy consumption models including three levels: theoretical, technical, and real. The energy leakage diagnosis approaches based on these models focused on evaluating energy consumption baselines at different levels. The authors [21], [22] suggested to build energy consumption baselines from historical data using artificial neural networks [23] and support vector machines [24], or to evaluate leakage risks from energy balances and mass balances [25], [26]. However, the authors found that these approaches have two deficiencies in real applications. First, the feasibility of isolating leaking machines is heavily dependent on energy measurements at the machine level. Second, the false alarm rate is high owing to the insensitivity of energy consumption caused by stochastic parallel jobs.
Biological immunity inspired the design of promising approaches for fault diagnosis in manufacturing fields [1], [27], [28], [29]. These approaches focused on designing conceptual frameworks for fault diagnosis with the inspiration from immune mechanisms (e.g. collaboration, pattern recognition, learning, and memory) [28], [30], [31], or on suggesting artificial immune algorithms for a specific machine or system fault diagnosis [32], [33], [34], [35]. However, only few immune approaches for hidden faults such as energy leakage or dealing with a jamming environment are found.
Therefore, for parallel-machine job shops, it is still a challenge to isolate leaking machines from a jamming environment with limited energy measurements, stochastic parallel jobs, and a fluctuating energy supply. In this study, three hypotheses are under consideration. First, an energy leakage is associated with individual machine in a parallel-machine job shop. Second, energy consumption at shop/group level and process parameters at machine level can be acquirable. Third, energy leakage can’t be accurately isolated by analysing jammed process parameters. Thereafter, an anti-jamming artificial immune approach (AJAI approach) that combined the danger model with an immune network is proposed. The main contributions of this paper rely on three main suggestions to overcome the jamming environment: a danger-model-inspired framework, an anti-jamming antigen feature, and an anti-jamming aiNet (AJ-aiNet) algorithm. The danger-model-inspired framework realizes the collaboration between danger (energy loss) detection at the shop level and antigen (process behaviour) isolation at the machine level. This dual activation strategy aims to suppress false alarm rates caused by the jamming environment. An anti-jamming antigen feature, called the difference in process behaviour fluctuation, is presented to enhance the separability between leaking and normal process behaviours. Antibody ageing and antigen killing strategies are embodied within aiNet to mitigate the disturbance of jamming antigens between leaking and normal clusters.
The paper is organized as follows. Section 2 overviews the main principles and applications of the danger model and artificial immune network. The proposed approach is presented in Section 3. The computer-based implementation is presented in Section 4. Section 5 introduces an application case. The experiments and results are described in Section 6. The advantages and limitations of the proposed approach are discussed in Section 7, and the conclusions drawn are presented in Section 8.
Section snippets
Danger model and artificial immune network
The proposed approach is inspired from the danger model and artificial immune network. In this section, the principles and applications related to these two immunity concepts are overviewed.
Framework
For parallel-machine job shops, energy leakage will be observed on both the energy consumption of the shop and process parameters of the machines, which are supposed to be available. With the inspiration from the danger model, the motivation of the proposed AJAI approach is to overcome the jamming environment by building a dual activation mechanism that combines the sensitivity of energy consumption and the isolation capability of process behaviours.
In analogy with the danger model proposed by
Computer-based implementation
The AJAI approach is driven by real time shop floor and machine data streaming, which are big data sets for large plants. In order to meet the real-time requirement of energy leakage diagnosis, an implementation is presented on the basis of computer cluster and concurrent streaming processing.
Application case
Steam leakage in tyre vulcanization shops is a typical case of energy leakage in parallel-machine job shops. The tyre vulcanization shop floor we studied in [22] is still adopted in this case. It is located in Guangzhou China, has 104 double mould vulcanizers, and vulcanizes rubber tyres of passenger vehicles and trucks. A vulcanization job forms a green tyre to the desired shape and converts it into a strong and elastic material under an elevated temperature and pressure [59], [60]. All
Experimental results
The proposed approach was developed using MATLAB 2013 Ra. Tests were performed on a PC Intel(R) Core(TM) i7 2.5 GHz with 8G of RAM, and data samples were selected from the application case mentioned in Section 5. The clustering performance of the AJ-aiNet algorithm and the diagnosis performance of the AJAI approach were evaluated.
The steam consumption of the studied vulcanization shop was measured using a steam meter. The evaluation of the physical steam consumption, technology steam
Discussion
In parallel-machine job shops, stochastic parallel jobs and a fluctuating energy supply lead to random fluctuation of parameters and produce serious jamming information for energy leakage diagnosis. The experimental results showed that the AJAI approach overcame the jamming environment and achieved an energy leakage diagnosis with acceptable diagnosis performance.
As a new feature, DPBF mitigates the impact of random fluctuation caused by jamming factors. Fig. 9 shows that DPBF make boundaries
Conclusion
This study presented an anti-jamming artificial immune approach to achieve energy leakage diagnoses in parallel-machine job shops. To overcome a jamming environment with limited energy measurements, stochastic parallel jobs, and a fluctuating energy supply, the proposed approach used anti-jamming strategies, namely a danger-model-inspired framework, an anti-jamming antigen feature, and an anti-jamming aiNet (AJ-aiNet) algorithm. The approach was developed using MATLAB 2013 Ra, and as an
Acknowledgements
The authors would like to thank the information management group of the vulcanization shop floor studied, who provided the experimental data and access to the database system. This work was supported by the National Natural Science Foundation of China (NSFC) (Grant No: 61702119 and 71401044).
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Cited by (3)
Application of Artificial Immune Systems in Advanced Manufacturing
2022, ArrayCitation Excerpt :The approach was validated in two industrial-oriented datasets regarding physical and network-related data. On the other hand, in manufacturing applications, Guo and Yang [116] (P19) proposes an anti-jamming ensemble approach that combines DT with AIN for distributed machine energy leakage diagnosis in complex environments such as parallel-machine job shops. In order to evaluate the proposed approach, several experiments were performed on a tyre vulcanization shop floor in China to diagnose the steam leakage of steam traps.
A hybrid intelligent algorithm for a fuzzy multi-objective job shop scheduling problem with reentrant workflows and parallel machines
2020, Journal of Intelligent and Fuzzy Systems