Safety Optimized Shift-Scheduling System based on Wireless Vibration Monitoring for Mechanical Harvesting Operations

• Main Authors: G. Aiello, A. Giallanza, I. Giovino
• Format: Article
• Language: English
• Published: AIDIC Servizi S.r.l. 2017-06-01
• Series: Chemical Engineering Transactions
• Online Access: https://www.cetjournal.it/index.php/cet/article/view/1323

Traditional approaches to vibration risk assessment are often carried out manually on the basis of reference data, whereas an automated approach, can yield more valuable information that can be beneficial for improving the health and safety conditions of the workers. In particular, the exposure to prolonged vibration is a potential cause of pathologies such as the hand-arm vibration syndrome (HAVS), therefore national and international regulations establish recommended limits to the workers’ exposure to vibration within an allowable daily limit. However, evaluating the maximum allowable operating time is not straightforward, therefore reference vibration values are provided by the manufacturer. These values are frequently unreliable, since the effective vibration intensity generated by mechanical machines largely depends upon several specific factors such as maintenance, operating conditions, etc. A more effective approach would rather involve equipping the worker with suitable instruments to monitor and analyse in real time the vibration exposure. This is particularly difficult in open-field operations as mechanical harvesting, where wireless sensing and communicating technologies can be effectively employed in general framework of the Internet of Things (IoT) to develop small monitoring devices at affordable cost. In such context, the present research proposes an innovative system aimed at estimating the hand-arm exposure to vibration according to the Standard EN ISO 5349-1:2004. In particular, the proposed system is based on a referenced monitoring system, based on the employment of a compact wearable unit to be attached to the waist of the operator and a fixed station for data storage and analysis. The paper describes how this information can be exploited in a decision context to effectively schedule the working shifts for a team of workers in high exposure operations such as olive harvesting with mechanical shakers. .