On the use of Wireless Sensor Networks in Preventative Maintenance for Industry 4.0

The goal of this paper is to present a literature study on the use of Wireless Sensor Networks (WSNs) in Preventative Maintenance applications for Industry 4.0. Requirements for industrial applications are discussed along with a comparative of the characteristics of the existing and emerging WSN technology enablers. The design considerations inherent to WSNs becoming a tool to drive maintenance efficiencies are discussed in the context of implementations in the research literature and commercial solutions available on the market

Vibration characterisation for fault detection and isolation in linear synchronous motor based conveyor systems

Linear synchronous motor (LSM) based transport systems are increasingly deployed in automated manufacturing environments. The aim of the study is to establish the feasibility of employing low power and low-cost vibration sensing cyber physical systems to perform near real-time fault detection and isolation for passive LSM vehicles. Empirical data capture was conducted on an LSM test-bed where vehicle velocity was varied to determine how changes in velocity would impact the vibration profile of the LSM vehicle. The recorded data was analyzed, and peak accelerations were examined for each of the velocities under study. Frequency domain analysis was conducted on the collated accelerometer data and frequencies of interest were identified. The findings are shown to concur with the manufacturer's operating specifications (0-30 Hz). A relationship between LSM vehicle speed and vibration frequency was established. The results presented provide the basis for the establishment of low-cost condition based preventative maintenance, deployed to a LSM based transport system for high volume manufacturing

On the potential for Electromagnetic Energy Harvesting for a Linear Synchronous Motor based Transport System in Factory Automation

Transport systems incorporating linear synchronous motors (LSMs) enable linear motion at high speed for emerging factory automation applications. The goal of this work is to determine the feasibility of harvesting energy directly from an operational LSM transport system employed in high volume manufacturing. Microelectromechanical (MEMs) based sensor technology, deployed as part of a wireless cyber physical system (CPS), perform near real-time magnetic field measurement for a mobile LSM vehicle. The vehicle under study is purposed for mobile factory automation and is not wired for communications nor does it have an onboard power source. A series of experiments were designed and conducted to establish the magnetic profile of the system. Empirical data capture was conducted on a cycled LSM test-bed comprising of 2 shuttles and 2 x 3 meter lengths of LSM track (MagneMotion QuickStick®100). Varying vehicle speeds were incorporated in the experimental regime to determine how changes in velocity would impact the magnetic profile of the vehicle. The recorded magnetic field data was analysed and a relationship between LSM vehicle speed and magnetic field frequency was established. The study highlights the potential to employ a single receiving coil to enable energy recovery which in turn could power a cyber-physical system (CPS) tasked with performing condition based monitoring of the LSM transport vehicles. This in turn can form the basis for the development of a predictive maintenance system, deployed to an LSM based transport layer in high volume manufacturing environments

Social Internet of Industrial Things for Industrial and Manufacturing Assets

The IoT (Internet of Things) concept is being widely discussed as the major approach towards the next industry revolution - Industry 4.0. As the value of data generated in social networks has been increasingly recognised, the integration of Social Media and the IoT is witnessed in areas such as product-design, traffic routing, etc.. However, its potential in improving system-level performance in production plants has rarely been explored. This paper discusses the feasibility of improving system-level performance in industrial production plants by integrating social network into the IoT concept. We proposed the concept of SIoIT (Social Internet of Industrial Things) which enables the cooperation between assets by sharing status data and optimal operation and maintenance decision-making via analysis of these data. We also identified the building blocks of SIoIT and characteristics of one of its important components - Social Assets. Related existing work is studied and future work towards the actual implementation of SIoIT is then discussed

Intelligent decision support for maintenance: an overview and future trends

The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions

The intelligent industry of the future: A survey on emerging trends, research challenges and opportunities in Industry 4.0

Strongly rooted in the Internet of Things and Cyber-Physical Systems-enabled manufacturing, disruptive paradigms like the Factory of the Future and Industry 4.0 envision knowledge-intensive industrial intelligent environments where smart personalized products are created through smart processes and procedures. The 4th industrial revolution will be based on Cyber-Physical Systems that will monitor, analyze and automate business processes, transforming production and logistic processes into smart factory environments where big data capabilities, cloud services and smart predictive decision support tools are used to increase productivity and efficiency. This survey provides insights into the latest developments in these domains, and identifies relevant research challenges and opportunities to shape the future of intelligent manufacturing environments.status: publishe

Standards-based wireless sensor networks for power system condition monitoring

This paper assesses the industrial needs motivating interest in wireless monito ring within the power industry, and reviews applications of WSN technology for substation condition monitoring (Section 2). A key contribution is the identification of a set of technical requirements for substation - based WSNs, focused around security requi rements, robustness to RF noise, and other utility - specific concerns (Section 3). Section 4 comprehensively assesses the suitability of various IWSN protocols for substation environments, using these requirements. A case study implementation of one standar d, ISA100.11a, is reported in Section 5, along with deployment experience. The paper concludes by describing future research challenges for WSN protocols which are specific to this domain

EARLY-WARNING PREDICTION FOR MACHINE FAILURES IN AUTOMATED INDUSTRIES USING ADVANCED MACHINE LEARNING TECHNIQUES

This Culminating Experience Project explores the use of machine learning algorithms to detect machine failure. The research questions are: Q1) How does the quality of input data, including issues such as outliers, and noise, impact the accuracy and reliability of machine failure prediction models in industrial settings? Q2) How does the integration of SMOTE with feature engineering techniques influence the overall performance of machine learning models in detecting and preventing machine failures? Q3) What is the performance of different machine learning algorithms in predicting machine failures, and which algorithm is the most effective? The research findings are: Q1) Effective outlier handling is vital for predictive maintenance as the variables distribution initially showed a right-skewed pattern but after rectifying, it became more centralized, with correlations between specific sensors showing potential for further exploration. Q2) Data balancing through SMOTE and feature engineering is essential due to the rarity of actual failure instances. Substantial challenges are observed when predicting \u27Failure\u27 instances, with a lower true positive rate (73%), resulting in low precision (0.02) and recall (0.73) for \u27Failure\u27 predictions. This is further reflected in the low F1-Score (0.03) for \u27Failure,\u27 indicating a trade-off between precision and recall. Despite a commendable overall accuracy of 94%, the class imbalance within the dataset (92,200 \u27Running\u27 instances vs. 126 \u27Failure\u27 instances) remains a contributing factor to the model\u27s limitations. Q3) Machine learning algorithm performance varies, with Catboost excelling in accuracy and failure detection. The choice of algorithm and continuous model refinement are critical for enhanced predictive accuracy in industrial contexts. The main conclusions are: Q1) Addressing outliers in data preprocessing significantly enhances the accuracy of machine failure prediction models. Q2) focuses on addressing the issue of equipment failure parameter imbalance. It was found in the research findings that there was a significant imbalance in the failure data, with only 0.14% of the dataset representing actual failures and 99.86% of the dataset pertaining to non-failure data. This extreme class disparity can result in biased models that underperform on underrepresented classes, which is a common problem in machine learning. Q3) Catboost outperforms other algorithms in predicting machine failures with remarkable accuracy and failure detection rates of 92% accuracy and 99% times it is correct, and further exploration of diverse data and algorithms is needed for tailored industrial applications. Future research areas include advanced outlier handling, sensor relationships, and data balancing for improved model accuracy. Addressing rare failures, enhancing model performance, and exploring diverse machine learning algorithms are critical for advancing predictive maintenance

Industry 4.0 – LabVIEW Based Industrial IoT Condition Monitoring System

As a result of a substantial shift in focus towards a more digital industry, multiple sectors of industry are now realising the potential of Industry 4.0 and Internet of Things (IoT) technology. The manufacturing industry in particular is subject to unexpected machine downtime from component wear over an extended period. With Industrial IoT (IIoT) technology implemented, there is the potential for gathering large quantities of data, which can be used for preventative maintenance. This research article addresses some of the technological requirements for developing an IoT industrial condition monitoring network, whose composition makes use of wireless devices along with conventional wired methods to enable a series of data capture and control operations in amongst a network of nodes. To provide a platform to host these operations, the industry standard fieldbus protocol Modbus TCP was used in conjunction with the LabVIEW development environment, where a bespoke graphical user interface was developed to provide control and a visual representation of the data collected. In addition, one of the nodes acted as the output for hardware displays, which in turn correlated the alarm status of the user interface. By using industry standard communication protocols, it was also possible to enable connectivity between real industry hardware, further extending the capabilities of the system