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As the process of products is developing, the contradiction between dimension quality and manufacturing cost should be balanced carefully. Excessive demands for higher quality would cause increase the production cost. Therefore, this paper will propose a new method of tolerance allocation based on NSGAIII. At the beginning, the assembly precedence diagram and subassembly identification are used to obtain feasible assembly sequences. The assembly variation analysis model of the conceptual design stage was established. Afterwards, the assembly tolerance function was used to analyze the quality of the assembly, and the exponential function was selected as the cost model. Based on the above functions, a multi-objective optimization model was established. The NSGAIII algorithm occurred, which can be used to optimize the tolerance allocation of the selected sequence, the optimal assembly sequence and the Pareto solution set of the multi-objective function. Finally, the auto-body floor can be used to illustrate the flowchart of tolerance allocation. The optimal solution is selected from the Pareto solution by the TOPSIS method. The results verify the feasibility and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The PID controller is the most common controller in industrial applications today, and it is likely to be the most important controller. In the paper, proposed an ALO algorithm and ANN controller is utilized for enhancing the performance of PID controller and control the tuning of time delay system. Based on the controlling parameters, the time delay system is controlled and low overshoot and fast settling time is achieved for different delay instants. The novelty of the proposed technique is improving the performance of PID controller by optimizing the PID gain parameters and controlling the higher order time delay system. The time delay system performance can be improved by minimizing error, tracking, time delay & error, fast and accurately to their respective reference values. With the optimal values for control parameter of time delay system, its performance can be significantly improved. In order to analyze the characteristics of the proposed method, the various time delay systems are analyzed. From the tuning system, the inputs and gain parameters are initialized to determine the objective function. Based on the proposed method, the optimal results is achieved and evaluated the rise time, overshoot, settling time and steady state error in time delay system. The proposed controller is implemented in the MATLAB/Simulink platform and the effectiveness of the proposed technique is analyzed through the performance indexes and the time domain specifications are evaluated using proposed technique and compared with the existing methods such as Artificial Bee colony Algorithm (ABC), Gravitational Search Algorithm (GSA) and Firefly Algorithm (FA). [ABSTRACT FROM AUTHOR]

Support Vector Machine (SVM) is an excellent technique for pattern recognition. This paper uses a multi-class SVM as a classifier to solve a multi-class classification problem for failure diagnosis. As the pre-defined parameters in the SVM influence the performance of the classification, this paper uses the heuristic Ant Colony Optimization (ACO) algorithm to find the optimal parameters. This multi-class SVM and ACO are applied to the failure diagnosis of an electric motor used in a railway system. A case study illustates how efficient the ACO is in finding the optimal parameters. By using the optimal parameters from the ACO, the accuracy of the performed diagnosis on the electric motor is found to be highest. [ABSTRACT FROM AUTHOR]

This paper presents a scalable Watchdog Agent-based toolbox approach for machine health prognostics. The toolbox consists of modularized embedded algorithms for signal processing and feature extraction, performance assessment, diagnostics and prognostics, which can be reconfigured for different machinery prognostic applications, and can be extensible and adaptable to most real-world machine situations. A decision making technique, Quality Function Deployment (QFD)-based tool selection method, is applied for the automatic selection of algorithms from the Watchdog Agent toolbox using multiple criteria. In addition, the architecture for the Watchdog Agent-based real-time remote machinery prognostics and health management, which incorporates remote and embedded predictive maintenance technologies, is presented. An industrial case involving the automatic tool changer of a machine tool is presented to illustrate how the Watchdog Agent toolbox can be used in diverse scenarios. [ABSTRACT FROM AUTHOR]

This paper addresses the weak fault periodic oscillatory feature extraction from vibration signals using Periodicity-induced Overlapping Group Shrinkage (POGS). Incipient signals are considered as periodic group sparse signals submerged in noise. Based on the minimization of a convex cost function involving a group sparsity promoting penalty function and the principle of majorization-minimization, a fast iterative algorithm is engaged for its solution. The method is applied to analyse gearbox vibration signals which was acquired from a machinery fault simulator. The results demonstrate that the method can satisfactorily reveal periodic impulses. [ABSTRACT FROM AUTHOR]

In this paper, rolling bearing signal denoise method based on EMD sample entropy is proposed. The vibration signal of a rolling bearing is decomposed into a number of IMF components using EMD method. IMF highlights the various components of the local characteristics of the signal, comprising the main bearing fault features. The optimal reconstructed signal is determined by calculating the cross-correlation coefficient between the reconstructed signal and the original signal. Finally, combined with the spectral kurtosis algorithm and resonance demodulation method, the optimal reconstructed signal is utilized to diagnose the fault status of rolling bearing. The effectiveness of the proposed method is experimentally verified and confirmed. [ABSTRACT FROM AUTHOR]