Babak Safaei

Bio: Babak Safaei is an academic researcher from Eastern Mediterranean University. The author has contributed to research in topics: Materials science & Nonlinear system. The author has an hindex of 29, co-authored 94 publications receiving 1971 citations. Previous affiliations of Babak Safaei include University of Johannesburg & Tsinghua University.

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Abstract: Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

Learning without Forgetting

Abstract: When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance.

CANintelliIDS: Detecting In-Vehicle Intrusion Attacks on a Controller Area Network Using CNN and Attention-Based GRU

Abstract: Controller area network (CAN) is a communication protocol that provides reliable and productive transmission between in-vehicle nodes continuously. CAN bus protocol is broadly utilized standard channel to deliver sequential communications between electronic control units (ECUs) due to simple and reliable in-vehicle communication. Existing studies report how easily an attack can be performed on the CAN bus of in-vehicle due to weak security mechanisms that could lead to system malfunctions. Hence the security of communications inside a vehicle is a latent problem. In this paper, we propose a novel approach named CANintelliIDS, for vehicle intrusion attack detection on the CAN bus. CANintelliIDS is based on a combination of convolutional neural network (CNN) and attention-based gated recurrent unit (GRU) model to detect single intrusion attacks as well as mixed intrusion attacks on a CAN bus. The proposed CANintelliIDS model is evaluated extensively and it achieved a performance gain of 10.79% on test intrusion attacks over existing approaches.

The recent progress of functionally graded CNT reinforced composites and structures

Abstract: In the last decade, the functionally graded carbon nanotube reinforced composites (FG-CNTRCs) have attracted considerable interest due to their excellent mechanical properties, and the structures made of FG-CNTRCs have found broad potential applications in aerospace, civil and ocean engineering, automotive industry, and smart structures. Here we review the literature regarding the mechanical analysis of bulk CNTR nanocomposites and FG-CNTRC structures, aiming to provide a clear picture of the mechanical modeling and properties of FG-CNTRCs as well as their composite structures. The review is organized as follows: (1) a brief introduction to the functionally graded materials (FGM), CNTRCs and FG-CNTRCs; (2) a literature review of the mechanical modeling methodologies and properties of bulk CNTRCs; (3) a detailed discussion on the mechanical behaviors of FG-CNTRCs; and (4) conclusions together with a suggestion of future research trends.