• Journal of the Korea Safety Management & Science
• /
• v.25 no.4
• /
• pp.141-151
• /
• 2023

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.

• Journal of Power Electronics
• /
• v.18 no.1
• /
• pp.103-115
• /
• 2018

Parametric uncertainties and inverter nonlinearity exist in the permanent magnet synchronous motor (PMSM) drive system of electrical vehicles, which may lead to performance degradation or failure, and eventually threaten reliable operation. Therefore, a model-free deadbeat predictive current controller (MFDPCC) for PMSM drive systems is proposed in this study. The data-driven ultra-local model of a surface-mounted PMSM (SMPMSM) drive system that consists of parametric uncertainties and inverter nonlinearity is first established through the input and output data of a SMPMSM drive system. Subsequently, MFDPCC is designed. The performance comparisons and analyses of the proposed MFDPCC, the conventional proportional-integral controller, and the model-based deadbeat predictive current controller for SMPMSM drive systems are implemented via system simulation and experimental tests. Results show the effectiveness and technical advantages of the proposed MFDPCC.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.33 no.11
• /
• pp.48-56
• /
• 2005

Predictive filter theory proposed recently can be characterized by inherent advantages of estimating modelling error and overcoming the disadvantage of the Kalman filter theory. A one-step ahead error is minimized to produce optimized filter performance in the form of the predictive filter. The main advantage of this filter lies in the ability to estimate both state vector and system model error. In this paper, attitude estimation results based upon the predictive filter theory is addressed. Mathematical formulation for estimating bias signal is peformed by using the predictive filter theory, and attitude estimation based upon vector observation is presented. From the results of this study, the potential applicability of the predictive filter is highlighted.

• Proceedings of the KIPE Conference
• /
• 2001.10a
• /
• pp.336-340
• /
• 2001

In a digital system, there are inevitable delays in calculations and applying the inverter output voltages to the motor terminals. Because of the delays, the conventional predictive current controller implemented in the digital system has large overshoot and large harmonics. A new predictive current controller, considering the delays, for a permanent magnet synchronous motor (PMSM) is presented. The effectiveness and feasibilities are shown by experimental results.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.143-143
• /
• 2000

A neural net based generalized predictive control(NNGPC) is presented for a hydraulic servo position control system. The proposed scheme employs generalized predictive control, where the future output being generated from the output of artificial neural networks. The proposed NNGPC does not require an accurate mathematical model for the nonlinear hydraulic system and takes less calculation time than GPC algorithm if the teaming of neural network is done. Simulation studies have been conducted on the position control of a hydraulic motor to validate and illustrate the proposed method.

• International journal of advanced smart convergence
• /
• v.12 no.3
• /
• pp.221-232
• /
• 2023

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational reliability of the ICT/Cold-Chain Unmanned Storage, a predictive maintenance system was implemented based on the LSTM model. In this paper, a server for data management, such as collection and monitoring, and an analysis server that notifies the monitoring server through data-based failure and defect analysis are separately distinguished. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on RabbitMQ, loading data in an InMemory method using Redis, and managing snapshot data DB in real time. The predictive maintenance platform can contribute to securing reliability by identifying potential failures and defects that may occur in the operation of the ICT/Cold-Chain Unmanned Storage in the future.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.25 no.9
• /
• pp.1039-1045
• /
• 1988

In the original incremental generalized predictive control, the receding horizon predictive control is introduced as a control law. But in this paper, we propose a generalized predictive self-tuning control using full-valued incremental controls. The control law is a mean horizon predictive control. The effectiveness of this algorithm in a variable time delay or load disturbances environment is demonstrated by computer simulation. The controlled plant is a nonminimum phase system.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.43 no.4
• /
• pp.627-637
• /
• 1994

In this study, a predictive fuzzy control algorithm to supervise the elevator system with plural cars is developed and its performance is evaluated. The proposed algorithm is based on fuzzy in-ference system to cope with multiple control objects and uncertainty of system state. The control objects are represented as linguistic predictive fuzzy rules and simplified reasoning method is utilized as a fuzzy inference method. Real-time simulation is performed with respect o all possible modes of control, and the resultant controls ard predicted. The predicted rusults are then utilized as the control in-puts of the fuzzy rules. The feasibility of the proposed control algorithm is evaluated by graphic simulator on computer. Finallu, the results of graphic simulation is compared with those of a conventional group control algorighm.

• Journal of Advanced Marine Engineering and Technology
• /
• v.20 no.3
• /
• pp.286-286
• /
• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Journal of Advanced Marine Engineering and Technology
• /
• v.20 no.3
• /
• pp.154-161
• /
• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.