Reliability-Based Preventive Maintenance Strategy of Truck Unloading Systems

Mohammed, Awsan; Ghaithan, Ahmed M.; Al-Saleh, Mashel; Al-Ofi, Khalaf

doi:10.3390/app10196957

Cited by 8 publications

(7 citation statements)

References 30 publications

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“…For example, as mentioned in [5], the supply chain of petroleum products faces major challenges, i.e., demand growth and the complexity of fluid transportation. The petroleum supply chain contains multiple stations extending from oil wells in exploration and production areas to the final destination.…”

Section: Innovations In Maintenance Strategiesmentioning

confidence: 99%

“…Therefore, it is important to assess and evaluate the reliability of the unloading system in order to improve its availability. In this context, Mohammed et al [5] present the operation philosophy of the truck unloading system, failure modes of the components within the system, and a bottom-up approach to analyze the reliability of the system. In addition, it provides reliabil-ity data, such as failure rates and the mean time between failures of the system components.…”

Section: Innovations In Maintenance Strategiesmentioning

confidence: 99%

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Systems Engineering: Availability and Reliability

et al. 2022

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Section: Innovations In Maintenance Strategiesmentioning

confidence: 99%

Section: Innovations In Maintenance Strategiesmentioning

confidence: 99%

Systems Engineering: Availability and Reliability

et al. 2022

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“…Preventive maintenance (PM) is a calendar-based program in which very comprehensive test routines are applied to off-line equipment [10]. In a PM program, data are collected during both on-line and off-line periods.…”

Section: Planned Maintenancementioning

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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“…As it is explained earlier, the motivation of the paper is due the importance of finding the number of failures in the field of maintenance strategy. Some examples of such importance can be seen in [11][12][13][14][15][16] from various perspectives. It is often found that most of the problems in maintenance engineering are finding optimal strategies that minimize the cost of operation to manipulate the system as well as minimize the number of failures of the system (e.g., [17]).…”

Section: Literature Reviewmentioning

confidence: 99%

Computing the Number of Failures for Fuzzy Weibull Hazard Function

Husniah

Supriatna

2021

Mathematics

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The number of failures plays an important factor in the study of maintenance strategy of a manufacturing system. In the real situation, this number is often affected by some uncertainties. Many of the uncertainties fall into the possibilistic uncertainty, which are different from the probabilistic uncertainty. This uncertainty is commonly modeled by applying the fuzzy theoretical framework. This paper aims to compute the number of failures for a system which has Weibull failure distribution with a fuzzy shape parameter. In this case two different approaches are used to calculate the number. In the first approach, the fuzziness membership of the shape parameter propagates to the number of failures so that they have exactly the same values of the membership. While in the second approach, the membership is computed through the α-cut or α-level of the shape parameter approach in the computation of the formula for the number of failures. Without loss of generality, we use the Triangular Fuzzy Number (TFN) for the Weibull shape parameter. We show that both methods have succeeded in computing the number of failures for the system under investigation. Both methods show that when we consider the function of the number of failures as a function of time then the uncertainty (the fuzziness) of the resulting number of failures becomes larger and larger as the time increases. By using the first method, the resulting number of failures has a TFN form. Meanwhile, the resulting number of failures from the second method does not necessarily have a TFN form, but a TFN-like form. Some comparisons between these two methods are presented using the Generalized Mean Value Defuzzification (GMVD) method. The results show that for certain weighting factor of the GMVD, the cores of these fuzzy numbers of failures are identical.

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Reliability-Based Preventive Maintenance Strategy of Truck Unloading Systems

Cited by 8 publications

References 30 publications

Systems Engineering: Availability and Reliability

Systems Engineering: Availability and Reliability

Automatic Maintenance Alert System for Heavy Duty Haulage Machines

Computing the Number of Failures for Fuzzy Weibull Hazard Function

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