An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines.

The feasibility and usefulness of frequency domain fusion of data from multiple vibration sensors installed on typical industrial rotating machines, based on coherent composite spectrum (CCS) as well as poly-coherent composite spectrum (pCCS) techniques, have been well-iterated by earlier studies. However, all previous endeavours have been limited to rotor faults, thereby raising questions about the proficiency of the approach for classifying faults related to other critical rotating machine components such as gearboxes. Besides the restriction in scope of the founding CCS and pCCS studies on rotor-related faults, their diagnosis approach was manually implemented, which could be unrealistic when faced with routine condition monitoring of multi-component industrial rotating machines, which often entails high-frequency sampling at multiple locations. In order to alleviate these challenges, this paper introduced an automated framework that encompassed feature generation through CCS, data dimensionality reduction through principal component analysis (PCA), and faults classification using artificial neural network (ANN). The outcomes of the automated approach are a set of visualised decision maps representing individually simulated scenarios, which simplifies and illustrates the decision rules of the faults characterisation framework. Additionally, the proposed approach minimises diagnosis-related downtime by allowing asset operators to easily identify anomalies at their incipient stages without necessarily possessing vibration monitoring expertise. Building upon the encouraging results obtained from the preceding part of this approach that was limited to well-known rotor-related faults, the proposed framework was significantly extended to include experimental and open-source gear fault data. The results show that in addition to early established rotor-related faults classification, the approach described here can also effectively and automatically classify gearbox faults, thereby improving the robustness.

The potential of construction robotics to reduce airborne virus transmission in the construction industry in the UK and China.

This paper aims to identify construction robotics' potential to reduce airborne virus transmission, review factors limiting the technology's adoption and highlight how similar barriers have been addressed in other industries. Construction robotics were identified and classified into 8 themes with 25 categories through a critical literature review. We undertook interviews with 4 construction contractors and conducted an online questionnaire with 32 experts from the UK (n=14) and China (n=18) who reviewed the robotic systems we identified and ranked the potential ability of each to reduce airborne virus transmission within the construction industry. The results of this study showed that construction robotics is not only beneficial to reduce airborne virus transmission, but may also help to reduce the spread of future contagious viruses. We found no significant difference (P>0.05) in practical usage and implementation barriers to construction robotics between the UK and China. Cost, training and limited awareness of robotic technologies were the main implementation barriers we identified in both countries. Both the UK and China may need to adopt strategies such as providing more financial support to small construction industries and skill training which are utilised successfully in other sectors to realise the potential of construction robotic technologies.

A systematic review of the application of immersive technologies for safety and health management in the construction sector.

INTRODUCTION: The construction industry employs about 7% of global manpower and contributes about 6% to the global economy. However, statistics have depicted that the construction industry contributes significantly to workplace fatalities and injuries despite multiple interventions (including technological applications) implemented by governments and construction companies. Recently, immersive technologies as part of a suite of industry 4.0 technologies, have also strongly emerged as a viable pathway to help address poor construction occupational safety and health (OSH) performance. METHOD: With the aim of gaining a broad view of different construction OSH issues addressed using immersive technologies, a review on the application of immersive technologies for construction OSH management is conducted using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) approach and bibliometric analysis of literature. This resulted in the evaluation of 117 relevant papers collected from three online databases (Scopus, Web of Science, and Engineering Village). RESULTS: The review revealed that literature have focused on the application of various immersive technologies for hazard identification and visualization, safety training, design for safety, risk perception, and assessment in various construction works. The review identified several limitations regarding the use of immersive technologies, which include the low level of adoption of the developed immersive technologies for OSH management by the construction industry, very limited research on the application of immersive technologies for health hazards, and limited focus on the comparison of the effectiveness of various immersive technologies for construction OSH management. CONCLUSIONS AND PRACTICAL APPLICATIONS: For future research, it is recommended to identify possible reasons for the low transition level from research to industry practice and proffer solutions to the identified issues. Another recommendation is the study of the effectiveness of the use of immersive technologies for addressing health hazards in comparison to the conventional methods.