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.

The unprecedented progress in computational technologies led to a substantial proliferation of artificial intelligence applications, notably in the era of big data and IoT devices. In the face of exponential data growth and complex computations, conventional computing encounters substantial obstacles pertaining to energy efficiency, computational speed, and area. Due to the diminishing advantages of technology scaling and increased demands from computing workloads, novel design techniques are required to increase performance and decrease power consumption. Approximate computing, nowadays considered a promising paradigm, achieves considerable improvements in overhead cost reduction (i.e., energy, area, and latency) at the expense of a modest (i.e., still acceptable) deterioration in application accuracy. Therefore, approximate computing at different levels (Data, Circuit, Architecture, and Software) has been attracted by the research and industrial communities. This paper presents a comprehensive review of the major research areas of different levels of approximate computing by exploring their underlying principles, potential benefits, and associated trade-offs. This is a burgeoning field that seeks to balance computational efficiency with acceptable accuracy. The paper highlights opportunities where these techniques can be effectively applied, such as in applications where perfect accuracy is not a strict requirement. This paper presents assessments of applying approximate computing techniques in various applications, especially machine learning algorithms (ML) and IoT. Furthermore, this review underscores the challenges encountered in implementing approximate computing techniques and highlights potential future research avenues. The anticipation is that this survey will stimulate further discourse and underscore the necessity for continued research and development to fully exploit the potential of approximate computing.

This study presents a control design of an angular position for the exoskeleton knee assistance system based on a model reference adaptive control (MRAC) strategy. Three schemes of the MRAC design have been proposed: the classical MRAC, MRAC with an adaptive disturbance observer, and MRAC with a nonlinear observer. The stability analysis for each scheme has been conducted and developed based on the Lyapunov theorem to prove the uniform ultimate bound of tracking and estimation errors. In addition, the adaptive laws have been developed for the proposed schemes according to the stability analysis. The effectiveness of the proposed state and output feedback controllers has been verified via computer simulation. The results based on numerical simulation have shown that the MRAC with a nonlinear observer could give better robustness characteristics and better performance in terms of tracking and estimation errors as compared to the other controllers.

Electrical power demands have increased significantly over the last years due to rapid increase in air conditioning units and home appliances per domestic unit, particularly in Iraq. Having an uninterrupted power supply is essential for the continuity of power-generated home services and industrial platforms. Currently, in Iraq, electrical power interruption has become a big concern to the utility suppliers. Despite successive attempts to put an end to this dilemma, the issue still prevails. One of the main factors in power outages in local zones is persistent faults in distribution transformers (DTs). DT is considered one of the main elements in the electrical network that is essential for the reliability of the grid supply. Due to the internal lack of monitoring system and periodic maintenance, DT is relentlessly subject to faults due to high overhead utilization. Therefore, in order to enhance the grid reliability, transformer health check, and maintenance practices, we propose a remote condition Internet of Things monitoring and fault prediction system that is based on a customized software-defined networking (SDN) technology. This approach is a transition to smart grid implementation by fusing the power grid with efficient and real-time wireless communication architecture. The SDN implementation is considered in two phases: one is a controller installed per local zone and the other is the main controller that is installed between zones and connected to the core network. The core network consists of redundant links to recover from any future fails. Furthermore, we propose a prediction system based on an artificial neural network algorithm, called distribution transformer fault prediction, that is installed in the management plane for periodic prediction based on real-time sensor traffic to our proposed cloud. Moreover, we propose a communication protocol in the local zone called local SDN-sense. The SDN-sense ensures a reliable communication and local node selection to relay DT sensor data to the main controller. Our proposed architecture showcases an efficient approach to handle future interruption and faults in power grid using cost-effective and reliable infrastructure that can predict and provide real-time health monitoring indices for the Iraqi grid network with minimal power interruptions. After extensive testing, the prediction accuracy was about 96.1%.

Analog electronic circuits play an essential role in many industrial applications and control systems. The traditional way of diagnosing failures in such circuits can be an inaccurate and time-consuming process; therefore, it can affect the industrial outcome negatively. In this paper, an intelligent fault diagnosis and identification approach for analog electronic circuits is proposed and investigated. The proposed method relies on a simple statistical analysis approach of the frequency response of the analog circuit and a simple rule-based fuzzy logic classification model to detect and identify the faulty component in the circuit. The proposed approach is tested and evaluated using a commonly used low-pass filter circuit. The test result of the presented approach shows that it can identify the fault and detect the faulty component in the circuit with an average of 98% F-score accuracy. The proposed approach shows comparable performance to more intricate related works.