The powerful tool for defect analysis is an expert system. It is a computer programme based on the knowledge of experts for solving the quality of castings. We present the expert system developed in the VSB-Technical University of Ostrava called ‘ESWOD’. The ESWOD programme consists of three separate modules: identification, diagnosis / causes and prevention / remedy. The identification of casting defects in the actual form of the system is based on their visual aspect.
The purpose of this research is to develop a Lean-RFID based waste identification system (LRWIS) for small-medium manufacturing companies. The specific objective of this research is to develop and implement the LRWIS from integrating the appropriate lean tools and advanced technologies for wastes reduction and inventory management. Subsequently, the framework was converted into a system for a small-medium sized wood processing manufacturer in Malaysia and integrated into a computerized program. The LRWIS can monitor real-time inventory and production status so the manufacturer can optimise the quantity of the primary products and deliver them on time as per the RFID information of each container. The manufacturer can also make decision instantly for controlling and changing different products in the production progress. The system provides simple constructed framework under a low cost infrastructure, yet it is of practical value in reducing the wastes and also optimising the production process.
The development of digital signal processors and the increase in their computing capabilities bring opportunities to employ algorithms with multiple variable parameters in active noise control systems. Of particular interest are the algorithms based on artificial neural networks. This paper presents an active noise control algorithm based on a neural network and a nonlinear input-output system identification model. The purpose of the algorithm is an active noise control system with a nonlinear primary path. The algorithm uses the NARMAX system identification model. The neural network employed in the proposed algorithm is a multilayer perceptron. The error backpropagation rule with adaptive learning rate is employed to update the weight of the neural network. The performance of the proposed algorithm has been tested by numerical simulations. Results for narrow-band input signals and nonlinear primary path are presented below.
The paper presents proposal of a model of the fluidized bed boiler adapted for use in model-based controllers e.g. predictive, adaptive or internal model control (IMC). The model has been derived in the form of transfer function matrix which allows its direct implementation in the controller structure. Formulated model takes into consideration the principal cross-coupling between process variables which enables the opportunity to search for feasibility of decoupling control. The results of the identification of the dynamics of the 2 MW industrial bubbling fluidized bed boiler using the proposed model form was presented. According to the experimental data it was found that despite of introduced simplifications presented model allows the boiler behavior prediction.
The paper discusses possible applications of wireless technologies in support of lean manufacturing tools. The typology of lean tools is provided. It distinguishes three main categories, which are identiﬁcation and analysis of waste, improvement implementation, and process monitoring. The set of lean tools was analyzed in terms of information requirements. On the other hand, the typology of wireless technologies was discussed including RFID and Wi-Fi. The literature review of wireless technology applications for support of lean tools was conducted. The literature was systematically reviewed from the point of view of speciﬁc technologies and speciﬁc tools which were the subjects of the analyzed publications. Both typologies were synthesized to establish a framework for wireless technologies applications in the context of lean manufacturing implementation. It also could serve as a guideline for lean practitioners and implies future research directions. This paper is an extended version of paper published by .
In the areas of acoustic research or applications that deal with not-precisely-known or variable conditions, a method of adaptation to the uncertainness or changes is usually necessary. When searching for an adaptation algorithm, it is hard to overlook the least mean squares (LMS) algorithm. Its simplicity, speed of computation, and robustness has won it a wide area of applications: from telecommunication, through acoustics and vibration, to seismology. The algorithm, however, still lacks a full theoretical analysis. This is probabely the cause of its main drawback: the need of a careful choice of the step size - which is the reason why so many variable step size flavors of the LMS algorithm has been developed. This paper contributes to both the above mentioned characteristics of the LMS algorithm. First, it shows a derivation of a new necessary condition for the LMS algorithm convergence. The condition, although weak, proved useful in developing a new variable step size LMS algorithm which appeared to be quite different from the algorithms known from the literature. Moreover, the algorithm proved to be effective in both simulations and laboratory experiments, covering two possible applications: adaptive line enhancement and active noise control.
This article investigates unstable tiltrotor in hover system identification from flight test data. The aircraft dynamicswas described by a linear model defined in Body-Fixed-Coordinate System. Output Error Method was selected in order to obtain stability and control derivatives in lateral motion. For estimating model parameters both time and frequency domain formulations were applied. To improve the system identification performed in the time domain, a stabilization matrix was included for evaluating the states. In the end, estimates obtained from various Output Error Method formulations were compared in terms of parameters accuracy and time histories. Evaluations were performed in MATLAB R2009b environment.