Industry 4.0 is the latest technological age, in which recent technological developments are being integrated within industrial systems. Consequently, maintenance management of current industrial manufacturing systems is affected by the emergence of the technologies and features of Industry 4.0. This study aimed to conduct a comprehensive literature review to understand how Industry 4.0 technologies and features affect the various functions of maintenance management systems. The reviewing process was initiated by examining the most recent related literature in three different databases. In total, 54 articles were classified into three research categories. Then, the integration of the main functions and components of the adopted maintenance management model and the Industry 4.0 features and technologies were aligned, focusing on the driving force of predictive maintenance. The analysis focused mainly on the technical aspects of the integration process, including integration concepts and integration-assisting tools, identifying the main applications and highlighting the challenges identified in the analysed literature. The key findings were that the main functions of maintenance management systems are significantly influenced by different Industry 4.0 technologies, mainly artificial intelligence–machine learning, CPS, IoT, big data, augmented reality, and cloud computing, in terms of successful integration. Consequently, the overall system implied tangible improvements through the involvement of different Industry 4.0 features which promote real-time condition monitoring, enable data management and curation, increase coordination between various maintenance tasks, facilitate supervision through remote maintenance applications, and, overall, improve operations and productivity, reduce unplanned shutdowns and, as a result, reduce the associated costs. To provide research directions, examples, and methodologies for integrating the various maintenance management system functions with the cutting-edge Industry 4.0 technologies and features based on real and practical cases present in the reviewed literature, the review’s findings are comprehensively categorised and summarised.

/pdf/integration-of-maintenance-management-system-functions-with-2q58oytg.pdf

Integration of Maintenance Management System Functions with Industry 4.0 Technologies and Features—A Review

Spring-mass-damper (SMD) systems are considered a benchmark setup in vibration-based systems. The system is considered linear for certain ranges during an excitation. For a simple system, the model is considered a second order system with one of the following behaviours for an impulse/unit input: exponential, sinusoidal, polynomial, or combined from the previous performances. The performance depends highly on the values of the parameters including the values of the mass, spring constant, and viscous damper coefficient. In this brief paper, one of the new promising filtering techniques, referred to as the sliding innovation filter (SIF), is used to estimate the system trajectories including the position and velocity. The filter is known for being robust and stable when system parameters change, which makes it a suitable candidate when the SMD system crosses the ranges of its linearized model or one of the parameters changes significantly. To complicate the case, only one state is assumed to be measured, which is the position. In this paper, a revised formulation of the SIF with the Luenberger method is introduced for cases with fewer measurements than states. The results are compared with the well-known Kalman Filter (KF). The results demonstrate that the proposed filter works well with the presence of uncertainties.

Linear Estimation Strategies Applied to a Spring-Mass-Damper System

The emergence of smart contracts has led to the wider use of blockchain, which involves a large number of virtual currency transactions. Because smart contracts are inevitably written with some vulnerabilities, which makes them vulnerable to attacks that cause property damage, and existing detection techniques and static analysis methods mainly target known vulnerability detection. We design a machine learning-based unknown vulnerability detection scheme using opcode sequences. The scheme first obtains opcode sequences of the execution path of contract transactions in the Ethereum virtual machine (EVM) by replaying them in Ethereum. Then we use a combination of N-gram model and a vector weight penalty mechanism to extract opcode features. Finally, we validate the effectiveness of our scheme by three machine learning models, namely the K-Near Neighbor Algorithm (KNN), the Support Vector Machine (SVM), and the Logistic Regression (LR). The SVM model achieves an accuracy of 91.4% and F1-score of 75.3% for the detection of unknown vulnerabilities. 

Detecting Unknown Vulnerabilities in Smart Contracts with Binary Classification Model Using Machine Learning

EIoT-PBFT: A multi-stage consensus algorithm for IoT edge computing based on PBFT

Machine failure in modern industry leads to lost production and reduced competitiveness. Maintenance costs represent between 15% and 60% of the manufacturing cost of the final product, and in heavy industry, these costs can be as high as 50% of the total production cost. Predictive maintenance is an efficient technique to avoid unexpected maintenance stops during production in industry. Vibration measurement is the main non-invasive method for locating and predicting faults in rotating machine components. This paper reviews the techniques and tools used to collect and analyze vibration data, as well as the methods used to interpret and diagnose faults in rotating machinery. The main steps of this technique are discussed, including data acquisition, data transmission, signal processing, and fault detection. Predictive maintenance through vibration analysis is a key strategy for cost reduction and a mandatory application in modern industry.

/pdf/a-review-on-vibration-monitoring-techniques-for-predictive-7xfhvwqm.pdf

A Review on Vibration Monitoring Techniques for Predictive Maintenance of Rotating Machinery

This review covers the topics of cognitive dynamic systems and their definition following Simon Haykin’s work in the field as well as their application through cognitive radar, cognitive radio and cognitive control. Furthermore, the article presents the topic of cognitive IoT and discusses it under the lens of cognitive dynamic systems referencing research in the field. It also discusses the needs for interoperability between IoT architectures and the need to integrate cognitive radio with future IoT frameworks developments.

A Review of Cognitive Dynamic Systems and Cognitive IoT

Artificial feedforward neural networks (ANN) have been traditionally trained by backpropagation algorithms involving gradient descent algorithms. This is in order to optimize the network’s weights and parameters in the training phase to minimize the out of sample error in the output during testing. However, gradient descent (GD) has been proven to be slow and computationally inefficient in comparison with studies implementing the extended Kalman filter (EKF) and unscented Kalman filter (UKF) as optimizers in ANNs. In this paper, a new method of training ANNs is proposed utilizing the sliding innovation filter (SIF). The SIF by Gadsden et al. has demonstrated to be a more robust predictor-corrector than the Kalman filters, especially in ill-conditioned situations or the presence of modelling uncertainties. In this paper, we propose implementing the SIF as an optimizer for training ANNs. The ANN proposed is trained with the SIF to predict the Mackey-Glass Chaotic series, and results demonstrate that the proposed method results in improved computation time compared to current estimation strategies for training ANNs while achieving results comparable to a UKF-trained neural network.

Neural network training loss optimization utilizing the sliding innovation filter

To maximize efficiency, quality of products, and profits, it is important to maintain machine tools to reduce downtime and maximize output. Predictive maintenance is the most efficient method of condition monitoring and maintenance. An Internet of Things approach can help implement an autonomous predictive CM system in manufacturing facilities. The critical parameters of sensor selection, communication, and data analysis have been examined. The components that make up an effective IoT CM system have been discussed and analyzed. An IoT approach has been shown to eliminate the disadvantages of traditional manual CM approaches.

Predictive Maintenance and Condition Monitoring in Machine Tools: An IoT Approach

Blockchain applications go far beyond cryptocurrency. As an essential blockchain tool, smart contracts are executable programs that establish an agreement between two parties. Millions of dollars of transactions attract hackers at a hastened pace, and cyber-attacks have caused large economic losses in the past. Due to this, the industry is seeking robust and effective methods to detect vulnerabilities in smart contracts to ultimately provide a remedy. The industry has been utilizing static analysis tools to reveal security gaps, which requires an understanding and insight over all possible execution paths to identify known contract vulnerabilities. Yet, the computational complexity increases as the path gets deeper. Recently, researchers have been proposing ML-driven intelligent techniques aiming to improve the efficiency and detection rate. Such solutions can provide quicker and more robust detection options than the traditionally used static analysis tools. As of this publication date, there is currently no published survey paper on smart contract vulnerability detection mechanisms using ML models. In order to set the ground for further development of ML-driven solutions, in this survey paper, we extensively reviewed and summarized a wide variety of ML-driven intelligent detection mechanism from the following databases: Google Scholar, Engineering Village, Springer, Web of Science, Academic Search Premier, and Scholars Portal Journal. In conclusion, we provided our insights on common traits, limitations and advancement of ML-driven solutions proposed for this field.

A survey on ethereum smart contract vulnerability detection using machine learning

In modern industrial settings, the quality of maintenance efforts directly influence equipment’s operational uptime and efficiency. Condition monitoring is a common process employed for predicting the health of a technical asset, whereby a predictive maintenance strategy can be adopted to minimize machine downtime and potential losses. Throughout the field, machine learning (ML) methods have become noteworthy for predicting failures before they occur, thereby preventing significant financial costs and providing a safer workplace environment. These benefits from predictive maintenance techniques, are particularly useful in the context of military equipment. Such equipment is often significantly expensive, and untimely machine failure could result in significant human endangerment. In this paper, a prognostic model (PROGNOS) is proposed to predict military equipment’s remaining useful life (RUL) based on their monitoring signals. The main considerations of PROGNOS are expectation maximization tuned Kalman Filter (EM-KF) for signal filtering, a recently introduced feature extraction algorithm (PCA-mRMR-VIF), and predictive LSTM model with an adaptive sliding window. The viability and performance of the proposed model were tested on a highly complex competition dataset: the NASA aircraft gas turbine engine degradation dataset, wherein readings from multiple sensor channels were recorded for degrading machines. According to testing results, we can confidently say that the proposed PROGNOS model was viable and robust overall, proving its general usefulness on all military equipment that emit signals.

Naseem Alsadi

Papers

A Review of Cognitive Dynamic Systems and Cognitive IoT

Neural network training loss optimization utilizing the sliding innovation filter

Predictive Maintenance and Condition Monitoring in Machine Tools: An IoT Approach

A survey on ethereum smart contract vulnerability detection using machine learning

PROGNOS: an automatic remaining useful life (RUL) prediction model for military systems using machine learning