Data-Driven Insights on Time-to-Failure of Electromechanical Manufacturing Devices: A Procedure and Case Study

Castaño, Fernando; Cruz, Yarens J.; Villalonga, Alberto; Haber, Rodolfo E.

doi:10.1109/tii.2022.3216629

Cited by 20 publications

(11 citation statements)

References 40 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A supervised learning approach called random forest (RF) leverages the essential properties of several decision trees to help make judgments. 71 First, during the learning phase, the RF causes the creation of sole decision tree branches. The overall prognostic is then obtained by combining the predictions from all tree branches.…”

Section: Machine Learning: Random Forest Regressionmentioning

confidence: 99%

Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

Subramanyam,

Kotikula,

Bennehalli

et al. 2024

ACS Omega

View full text Add to dashboard Cite

Recent studies focus on enhancing the mechanical features of natural fiber composites to replace synthetic fibers that are highly useful in the building, automotive, and packing industries. The novelty of the work is that the woven areca sheath fiber (ASF) with different fiber fraction epoxy composites has been fabricated and tested for its tribological responses on three-body abrasion wear testing machines along with its mechanical features. The impact of the fiber fraction on various features is examined. The study also revolves around the development and validation of a machine learning predictive model using the random forest (RF) algorithm, aimed at forecasting two critical performance parameters: the specific wear rate (SWR) and the coefficient of friction (COF). The void fraction is observed to vary between 0.261 and 3.8% as the fiber fraction is incremented. The hardness of the mat rises progressively from 40.23 to 84.26 HRB. A fair ascent in the tensile strength and its modulus is also observed. Even though a short descent in flexural strength and its modulus is seen for 0 to 12 wt % composite specimens, they incrementally raised to the finest values of 52.84 and 2860 MPa, respectively, pertinent to the 48 wt % fiber-loaded specimen. A progressive rise in the ILSS and impact strength is perceptible. The wear behavior of the specimens is reported. The worn surface morphology is studied to understand the interface of the ASF with the epoxy matrix. The RF model exhibited outstanding predictive prowess, as evidenced by high R-squared values coupled with low mean-square error and mean absolute error metrics. Rigorous statistical validation employing paired t tests confirmed the model's suitability, revealing no significant disparities between predicted and actual values for both the SWR and COF.

show abstract

Section: Machine Learning: Random Forest Regressionmentioning

confidence: 99%

Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

Subramanyam,

Kotikula,

Bennehalli

et al. 2024

ACS Omega

View full text Add to dashboard Cite

show abstract

“…Fuzzy inference produces smaller output values as control error increases. The magnetic levitation ball system changes radically when the tracking signal changes violently 29 , 30 . To adjust to the change in control system state, the inverse model of PSO cannot converge quickly.…”

Section: Fuzzy Supervisory Controlmentioning

confidence: 99%

An intelligent fuzzy-particle swarm optimization supervisory-based control of robot manipulator for industrial welding applications

Kumar¹,

Naveen

Vijayakumar

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The propensity of manufacturers to produce goods at affordable cost, with more accuracy, and at a faster rate force them to search for novel solutions, such as deploying robots in place of people in a sector that can accommodate their needs. Welding is one of the most crucial processes in the automotive industry. This process is time-consuming, subject to error, and demands skilled professionals. The robotic application can improve this area of production and quality. Other industries, such as painting and material handling, can also profit from the use of robots. This work describes the fuzzy DC linear servo controller, which functions as a robotic arm actuator. Robots have been widely employed in most productive sectors in recent years, including assembly plates, welding, tasks at higher temperatures, etc. Controlling a robot accurately is a difficult undertaking as a robot is very nonlinear with many joints that are often organized and unstructured. To carry out the effective task, an effective PID control based on fuzzy logic has been employed together with the method of Particle Swarm Optimization (PSO) approach for the estimate of the parameter. This offline technique determines the lowest number of optimal robotic arm control parameters. To verify the controller design with computer simulation, a comparative assessment of controllers is given by means of a fuzzy surveillance controller with PSO which improves the parameter gain to provide a rapid climb, a smaller overflow, no steady condition error signal, and effective torque control of the robot arm.

show abstract

“…In 26 , authors have designed an optimal rule based fuzzy system with an improved genetic algorithm to control throttle valves for managed pressure drilling (MPD) systems. In 27 , a fuzzy system is developed for electromechanical devices to predict and classify faults. In 28 , a hybrid fuzzy expert system is proposed as a decision-support tool to lessen the risk linked with petrol gearbox stations.…”

Section: Introductionmentioning

confidence: 99%

A novel PID controller for pressure control of artificial ventilator using optimal rule based fuzzy inference system with RCTO algorithm

Acharya

Das

2023

Sci Rep

View full text Add to dashboard Cite

In order to improve the pressure tracking response of an artificial ventilator system, a novel proportional integral derivative (PID) controller is designed in the present work by utilizing an optimal rule-based fuzzy inference system (FIS) with a reshaped class-topper optimization algorithm (RCTO), which is named as (Fuzzy-PID). Firstly, a patient-hose blower-driven artificial ventilator model is considered, and the transfer function model is established. The ventilator is assumed to operate in pressure control mode. Then, a fuzzy-PID control structure is formulated such that the error and change in error between the desired airway pressure and actual airway pressure of the ventilator are set as inputs to the FIS. The gains of the PID controller (proportional gain, derivative gain, and integral gain) are set as outputs of the FIS. A reshaped class topper optimization algorithm (RCTO) is developed to optimize rules of the FIS to establish optimal coordination among the input and output variables of the FIS. Finally, the optimized Fuzzy-PID controller is examined for the ventilator under different scenarios such as parametric uncertainties, external disturbances, sensor noise, and a time-varying breathing pattern. In addition, the stability analysis of the system is carried out using the Nyquist stability method, and the sensitivity of the optimal Fuzzy-PID is examined for different blower parameters. The simulation results showed satisfactory results in terms of peak time, overshoot, and settling time for all cases, which were also compared with existing results. It is observed in the simulation results that the overshoot in the pressure profile is improved by 16% with the proposed optimal rule based fuzzy-PID as compared with randomly selected rules for the system. Settling time and peak time are also improved 60–80% compared to the existing method. The control signal generated by the proposed controller is also improved in magnitude by 80–90% compared to the existing method. With a lower magnitude, the control signal can also avoid actuator saturation problems.

show abstract

Data-Driven Insights on Time-to-Failure of Electromechanical Manufacturing Devices: A Procedure and Case Study

Cited by 20 publications

References 40 publications

Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

An intelligent fuzzy-particle swarm optimization supervisory-based control of robot manipulator for industrial welding applications

A novel PID controller for pressure control of artificial ventilator using optimal rule based fuzzy inference system with RCTO algorithm

Contact Info

Product

Resources

About