Maintenance optimization for a multi-unit system with digital twin simulation

Savolainen, Jyrki; Urbani, Michele

doi:10.1007/s10845-021-01740-z

Cited by 36 publications

(24 citation statements)

References 64 publications

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“…A multiscale (timescale and space scale) modeling method was proposed in Reference [54] for satellite assembly, integration, and test shop-floor modeling. For actualizing the DT model of a mine for maintenance optimization of multiple equipment, co-simulation of DES model and system dynamic cash-flow model was utilized in Reference [55] to study the influence of macroeconomic variables on the long-term time-based maintenance policy. A DES model was used to model the low-level interaction between equipment, while the cash-flow model was used to model high-level managerial profitability decisions.…”

Section: Digital Twinmentioning

confidence: 99%

Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems

Ademujimi

Prabhu

2022

Sensors

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Smart manufacturing systems are being advocated to leverage technological advances that enable them to be more resilient to faults through rapid diagnosis for performance assurance. In this paper, we propose a co-simulation approach for engineering digital twins (DTs) that are used to train Bayesian Networks (BNs) for fault diagnostics at equipment and factory levels. Specifically, the co-simulation model is engineered by using cyber–physical system (CPS) consisting of networked sensors, high-fidelity simulation model of each equipment, and a detailed discrete-event simulation (DES) model of the factory. The proposed DT approach enables injection of faults in the virtual system, thereby alleviating the need for expensive factory-floor experimentation. It should be emphasized that this approach of injecting faults eliminates the need for obtaining balanced data that include faulty and normal factory operations. We propose a Structural Intervention Algorithm (SIA) in this paper to first detect all possible directed edges and then distinguish between a parent and an ancestor node of the BN. We engineered a DT research test-bed in our laboratory consisting of four industrial robots configured into an assembly cell where each robot has an industrial Internet-of-Things sensor that can monitor vibrations in two-axes. A detailed equipment-level simulator of these robots was integrated with a detailed DES model of the robotic assembly cell. The resulting DT was used to carry out interventions to learn a BN model structure for fault diagnostics. Laboratory experiments validated the efficacy of the proposed approach by accurately learning the BN structure, and in the experiments, the accuracy obtained by the proposed approach (measured using Structural Hamming Distance) was found to be significantly better than traditional methods. Furthermore, the BN structure learned was found to be robust to variations in parameters, such as mean time to failure (MTTF).

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Section: Digital Twinmentioning

confidence: 99%

Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems

Ademujimi

Prabhu

2022

Sensors

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“…The function of maintenance is to ensure that plant and equipment are available in a satisfactory condition for operation when required. To prevent unforeseen circumstances and achieve a reliable and continuous operation of machines, there should be a maintenance plan which will be accepted by the staff of any company that uses this type of equipment [8]. There are various ways in which maintenance can be done and each of them depends on the activities performed during the process…”

Section: Maintenance Systemmentioning

confidence: 99%

Automatic Maintenance Alert System for Heavy Duty Haulage Machines

Addo

Dwomoh

Ofori

2022

JNTE

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The timely maintenance of haulage machines is important to prevent machine breakdown and reduce productivity down time. An unintended breakdown or machine stop due to inadequate maintenance causes huge financial loss to the company due to delivery delays. Excessive maintenance also increases production cost due to down time and workshop maintenance. Most haulage companies like Mantrac Ghana Limited have a maintenance schedule for their customers. However, most of these customers do not maintain these machines on time, largely due to forgetfulness or sheer disregard for the maintenance schedule. It is thus important to implement an automatic alert system to notify operators of this machinery when it is time for maintenance. This paper seeks to design an automatic alert system using ATmega328 microcontroller, SIM900 GSM module and alarm unit. A sensor connected to the engine of the haulage machine is used to monitor the condition and operational hours of the machine. The microcontroller is programmed using proteus software to receive instructions from the sensor and issue commands to activate both the GSM module and the buzzer simultaneously. The type and time of maintenance to be done is displayed on the LED display. A corresponding message is sent via the GSM module to both Mantrac Ghana Limited and the customer to undertake the type of maintenance. It can be concluded that an automatic alert system would improve strict adherence to maintenance schedule and help reduce machine.

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“…Later, Kumar et al [10] examined the behaviour of a bread-making system composed of five unique subsystems and assessed system characteristics beneficial to management using RPGT within steady-state conditions. Savolainen et al [11] studied a system configuration with three demand-dependent unit functionality. A non-markovian mechanism has been investigated using extra variables (Shakuntla et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Mathematical model for analysing availability of threshing combine machine under reduced capacity

Singla

Modibbo

Mijinyawa

et al. 2022

YUGOSLAV J OPERATION

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Obtaining system availability in an engineering design is trickish and challenging, especially when there is a reduction in capacity; however, it supports system maintainability. In this paper, a mathematical model for finding the availability under the reduced capacity has been proposed using the Chapman Kolmogorov approach with the help of transition diagrams associated with various possible combinations of probabilities. The paper observes the most critical subsystem by selecting variable failure and repair rates from different subsystems. It deals with the sensitivity analysis of a complex repairable threshing combined machine comprising subsystems in a series configuration and the threshing machine consisting of 21 subsystems. The device works in total capacity when the threshing drum and feeding Hooper work in the complete state, and the concave subsystem and blower work with reduced power. This study dealt with uncertain data and was analyzed analytically using a complex repairable system. The availability of the entire machine has been investigated analytically, and various availability indices such as subsystems extruder have been computed and reported. The study discovered that subsystem extruder has the most impact on some subsystems? overall system availability.

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Maintenance optimization for a multi-unit system with digital twin simulation

Cited by 36 publications

References 64 publications

Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems

Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems

Automatic Maintenance Alert System for Heavy Duty Haulage Machines

Mathematical model for analysing availability of threshing combine machine under reduced capacity

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