Remaining Useful Life (RUL) is used to provide an early indication of failures that required performing maintenance and/or replacement of the system in advance. Accurate RUL prediction offers cost-effective operation for decision-makers in the industry. The availability of data using intelligence sensors leverages the power of data-driven methods for RUL estimation. Deep Learning is one example of a data-driven method that has a lot of applications in the industry. One of these applications is the RUL prediction where DL algorithms achieved good results. This paper presents an Autoencoder-based Deep Belief Network (AE-DBN) model for Aircraft engines’ RUL estimation. The AE-DBN DL model is utilized the feature extraction characteristic of AE and superiority in learning long-range dependencies of DBN. The efficiency of the proposed DL algorithm is evaluated by comparison between the proposed AE-DBRN and the state-of-the-art related method for RUL perdition for four datasets. Based on the Root Mean Square Error (RMSE) and Score indices, the outcomes reveal that the AE-DBN RUL prediction model is superior to other DL approaches.

Predictive maintenance (PdM) is a successful strategy used to reduce cost by minimizing the breakdown stoppages and production loss. The massive amount of data that results from the integration between the physical and digital systems of the production process makes it possible for deep learning (DL) algorithms to be applied and utilized for fault prediction and diagnosis. This paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-LSTM approach enhances the predictive accuracy and also reduces the complexity of the model. To evaluate the proposed model, two comparisons with regular LSTM and gradient boosting decision tree (GBDT) methods using a freely available dataset have been made. The PdM model based on CNN-LSTM method demonstrates better prediction accuracy compared to the regular LSTM, where the average F-Score increases form 93.34% in the case of regular LSTM to 97.48% for the proposed CNN-LSTM. Compared to the related works the proposed hybrid CNN-LSTM PdM approach achieved better results in term of accuracy.

This paper presents a comparative study between two advance versions of the classical Proportional-Integral-Derivative (PID) controller including the Proportional Integral minus Proportional Derivative (PI-PD) controller and the Nonlinear Proportional Derivative (NPD) to manipulate the position of the two angulars of the two-links robot arm system and eliminate the effects of the load disturbances. The dynamic equations of the two-links robot arm system are obtained based on the Lagrange approach. To determine the best value of the adjustable coefficients of each controller, tuning process is converted to an optimization problem. Then, Bee Algorithm (BA) optimization technique is employed to find the best value of the adjustable coefficients of each controller. The computer simulation results based on MATLAB show the NPD-BA controller is outperform the PI-PD-BA controller in normal condition works. Furthermore, the NPD-BA demonstrates a substantial enhancement when the load disturbance is applied.

The production–inventory system is a problem of multivariable input and multivariant output in mathematics. Selecting the best system control parameters is a crucial managerial decision to achieve and dynamically maintain an optimal performance in terms of balancing the order rate and stock level under dynamic influence of many factors affecting the system operations. The dynamic performance of the popular APIOBPCS model and the newly modified 2APIOBPCS model for optimal control of production–inventory systems is examined in the study. This examination is based on the leveled ground with a new simulation scheme that incorporates a designated multi-objective particle swarm optimization (MOPSO) algorithm into the simulation, which enables the optimal set of system control parameters to be selected for achieving the situational best possible performance of the production–inventory system under study. The dynamic performance is measured by the variance ratio between the order rate and the sales rate related to the bullwhip effect, and the integral of absolute error related to the inventory responsiveness in response to a random customer demand. Our simulation indicates that the 2APIOBPCS model performed better than or at least no worse than, and more robust than the APIOBPCS model under different conditions.

Purpose: The purpose of this paper is to examine the impact of applying two classical controller strategies, including two proportional (P) controllers with two feedback loops and one proportional–integral–derivative (PID) controller with one feedback loop, on the order and inventory performance within a production-inventory control system. Design/methodology/approach: The simulation experiments of the dynamics behaviour of the production-inventory control system are conducted using a model based on control theory techniques. The Laplace transformation of an Order–Up–To (OUT) model is obtained using a state-space approach, and then the state-space representation is used to design and simulate a controlled model. The simulations of each model with two control configurations are tested by subjecting the system to a random retail sales pattern. The performance of inventory level is quantified by using the Integral of Absolute Error (IAE), whereas the bullwhip effect is measured by using the Variance ratio (Var). Findings: The simulation results show that one PID controller with one feedback loop outperforms two P controllers with two feedback loops at reducing the bullwhip effect and regulating the inventory level. Originality/value: The production-inventory control system is broken down into three components, namely: the forecasting mechanism, controller strategy and production-inventory process. A state-space approach is adopted to design and simulate the different controller strategy.

One of the most difficult systems to control is the ball and beam (BnB) system due to its under-actuation, instability, and nonlinearity. To address these challenges, this paper presents an application of using the nonlinear synergetic control (SC) algorithm for position tracking control of the BnB system. A swarm optimization method based on sea lion optimization (SLO) has also been used to achieve an optimum dynamic performance by adjusting the suggested controller’s parameter. The Integral Time of Absolute Errors (ITAE) is employed by the SLO as an objective function to adjust the design parameters of the suggested SC. Using MATLAB software, a comparison has been made between the SC controller and the classical state feedback controller (SFC) to test the effectiveness of the suggested control algorithm. The findings illustrate that the suggested SC offers better transient response in terms of reducing the settling time and the overshoot than SFC. The effect of the external disturbance has also been examined. It has been found that SC provides more robustness performance than SFC.