The oil and gas industry is looking for ways to accurately identify and prioritize the failure modes (FMs) of the equipment. Failure mode and effect analysis (FMEA) is the most important tool used in the maintenance approach for the prevention of malfunctioning of the equipment. Current developments in the FMEA technique are mainly focused on addressing the drawbacks of the conventional risk priority number calculations, but the group effects and interrelationships of FMs on other measurements are neglected. In the present study, a hybrid distribution risk assessment framework was proposed to fill these gaps based on the combination of modified linguistic FMEA (LFMEA), Analytic Network Process (ANP), and Decision Making Trial and Evaluation Laboratory (DEMATEL) techniques. The hybrid framework of FMEA was conducted in a hazardous environment at a power generation unit in an oil and gas plant located in Yemen. The results show that mechanical and gas leakage FM in electrical generators posed a greater risk, which critically affects other FMs within the plant. It was observed that the suggested framework produced a precise ranking of FMs, with a clear relationship among FMs. Also, the comparisons of the proposed framework with previous studies demonstrated the multidisciplinary applications of the present framework.

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In the present study, a rotational piezoelectric (PZT) energy harvester has been designed, fabricated and tested. The design can enhance output power by frequency up-conversion and provide the desired output power range from a fixed input rotational speed by increasing the interchangeable planet cover numbers which is the novelty of this work. The prototype ability to harvest energy has been evaluated with four experiments, which determine the effect of rotational speed, interchangeable planet cover numbers, the distance between PZTs, and PZTs numbers. Increasing rotational speed shows that it can increase output power. However, increasing planet cover numbers can increase the output power without the need to increase speed or any excitation element. With the usage of one, two, and four planet cover numbers, the prototype is able to harvest output power of 0.414 mW, 0.672 mW, and 1.566 mW, respectively, at 50 kΩ with 1500 rpm, and 6.25 Hz bending frequency of the PZT. Moreover, when three cantilevers are used with 35 kΩ loads, the output power is 6.007 mW, and the power density of piezoelectric material is 9.59 mW/cm3. It was concluded that the model could work for frequency up-conversion and provide the desired output power range from a fixed input rotational speed and may result in a longer lifetime of the PZT.

The electrical generation industry is looking for techniques to precisely determine the proper maintenance policy and schedule of their assets. Reliability-centered maintenance (RCM) is a methodology for choosing what maintenance activities have to be performed to keep the asset working within its designed function. Current developments in RCM models are struggling to solve the drawbacks of traditional RCM with regards to optimization and strategy selection; for instance, traditional RCM handles each failure mode individually with a simple yes or no safety question in which question has the possibility of major error and missing the effect of a combinational failure mode. Hence, in the present study, a hybrid RCM model was proposed to fill these gaps and find the optimal maintenance policies and scheduling by a combination of hybrid linguistic-failure mode and effect analysis (HL-FMEA), the co-evolutionary multi-objective particle swarm optimization (CMPSO) algorithm, an analytic network process (ANP), and developed maintenance decision tree (DMDT). To demonstrate the effectiveness and efficiencies of the proposed RCM model, a case study on the maintenance of an electrical generator was conducted at a Yemeni oil and gas processing plant. The results confirm that, compared with previous studies, the proposed model gave the optimal maintenance policies and scheduling for the electrical generator in a well-structured plan, economically and effectively.

In pursue of a comfortable vehicle driving in different road profiles, intelligent methods used to improve the suspension system of the vehicle. Semi-active suspension system outperformed other suspension systems because it contains an intelligent actuator that can give the appropriate force to dissipate unwanted vibration using intelligent and real-time controllers. Here in this paper MR fluid damper with Fuzzy-PID controller examined to optimized using modified DE algorithm. However, in the Fuzzy-PID controller, the fuzzy logic algorithm is used to autotune the PID controller, but it cannot be considered as a fully real-time controller since the fuzzy algorithm uses a previous knowledge base built offline. The offline design of Fuzzy algorithm cannot cope with real-time unexpected vibrations occurred while driving car in different road profiles. In this paper, a Fuzzy-DE-PID controller proposed based on modified DE algorithm to enhance the Fuzzy logic gains in order to increase vehicle semi-active suspension system performance. To prove the effectiveness of the proposed controllers, a simulation and experimental tests were conducted using the proposed controller with different disturbances. The results of the simulation and the experimental tests for the proposed controller showed improvement of the vehicle's ride comfort over the Fuzzy-PID and the passive system. We believe that using this proposed controller in any other real-time application will improve the performance to the highest levels without the need for a previous knowledge base for designing a realtime Fuzzy-PID controller.

Patients with hand tremors may find routine activities such as writing and holding objects affected. In response to this problem, an active control technique has been examined in order to lessen the severity of tremors. In this article, an online method of a hybrid proportional-integral control with active force control strategy for tremor attenuation is presented. An intelligent mechanism using iterative learning control is incorporated into the active force control loop to approximate the estimation mass parameter. Experiments were conducted on a dummy hand model placed horizontally in a tremor test rig. When activated by a shaker in the vertical direction, this resembles a postural tremor condition. In the proportional-integral plus active force control, a linear voice coil actuator is used as the main active tremor suppressive element. A sensitivity analysis is presented to investigate the robustness of the proposed controller in a real-time control environment. The findings of this study demonstrate that the intelligent active force control and iterative learning controller show excellent performance in reducing tremor error compared to classic pure proportional, proportional-integral and hybrid proportional-integral plus active force control controllers.

The Magneto-rheological (MR) fluid damper is prevalent in the field of semi-active suspension whose viscosity changes by the change of magnetic field passing through the damping fluid. In this study, a semi-active suspension quarter car model is employed as a plant. The Bingham model of MRF damper is exploited with PID and Fuzzy + PID controllers. The current is controlled by the controllers according to the quarter car chassis disturbance. The step road profile is used as an input disturbance to the suspension system. The displacement of sprung mass is analyzed in terms of time and frequency domain. The maximum power spectral density of acceleration for step response with Fuzzy + PID is reduced by 87.28 % as compared to passive suspension whereas PID reduced only 79.95 %. This indicates that the MRF damper with right tuned Fuzzy + PID controller provide a safer ride compared to PID controller and passive suspension.