Construction safety for ethnic minorities in Hong Kong

The construction industry of Hong Kong is experiencing an ageing problem and a labour shortage. To alleviate the shortage of construction workforce, the government has initiatives to assist ethnic minorities to join the construction industry. It is foreseeable that more people from the ethnic minorities will join the construction industry. The safety of workers from the ethnic minorities in construction has attracted growing research interest in many developed countries. Statistics show that workers from the ethnic minorities were nearly 30 percent more likely to have work-related injuries than local workers. However, in Hong Kong, official statistics on the safety of workers from the ethnic minorities are not available. This reflects the racial or ethnic insensitivity of the construction industry in Hong Kong. As the safety of ethnic minorities has not received the attention, this research seeking to contribute to efforts to improve the safety of workers from the ethnic minorities in the construction industry of Hong Kong is all the more urgent. This paper provides an initial report of a research project which focuses on improving the safety of ethnic minority construction workers. Qualitative and quantitative research methods applied in conducting the research are first discussed. Preliminary statistics of construction accidents involving ethnic minority construction workers will then be reported.

Fault detection of slow speed rolling element bearing with noise removal techniques

Rolling Element Bearings (REBs) are vital components in rotating machineries for providing rotating motion. In slow speed rotating machines, bearings are normally subjected to heavy static loads and a catastrophic failure can cause enormous disruption to production and human safety. Due to its low operating speed the impact energy generated by the rotating elements on the defective components is not sufficient to produce a detectable vibration response. This is further aggravated by the inability of general measuring instruments to detect and process the weak signals at the initiation of the defect accurately. Furthermore, the weak signals are often corrupted by background noise. This is a serious problem faced by maintenance engineers today and the inability to detect an incipient failure of the machine can significantly increases the risk of functional failure and costly downtime. This paper presents the application of noise removal techniques for enhancing the detection capability for slow speed REB condition monitoring. Blind deconvolution (BD) and adaptive line enhancer (ALE) are compared to evaluate their performance in enhancing the source signal with consequential removal of background noise. In the experimental study, incipient defects were seeded on a number of roller bearings and the signals were acquired using acoustic emission (AE) sensor. Kurtosis and modified peak ratio (mPR) were used to determine the detectability of signal corrupted by noise.