Prevention of Occupational Accidents and Occupational Diseases with Supervised Machine Learning Algorithms: Different Sector Applications

Abstract

Abstract − The Occupational health and safety is a discipline that prevents work accidents and occupational diseases with a proactive method. For employee health, countries have legal responsibilities within the scope of international conventions, and employers have national responsibilities. It is obligatory for employers to carry out risk assessments, provide occupational safety trainings, carry out inspections, employ occupational safety specialists and workplace physicians, and record all work regard work safety. In countries, inspections are carried out with labor inspectors and private companies provide occupational safety services. However, it is difficult for the authorities to monitor occupational safety in large industrial facilities such as petrochemicals and refineries, where the flow of workers, materials and work equipment is too much and very fast. As workplace capacity, number of employees and material flow increase, the type and number of work accidents and occupational diseases also increase. Artificial intelligence technologies facilitate these follow-ups. The purpose of this article is to investigate the proactive prevention of the factors that cause work accidents and occupational diseases with supervised machine learning algorithms in different sectors. A literature search was conducted on sciencedirect, scopus, googlescholar databases. It has been examined what kind of algorithms are used in which sectors. According to the studies in the literature and applications in different sectors, the data collected with sensors and stored with cloud computing are fed to the relevant supervised machine learning algorithms that have been trained and tested before, and the factors that cause work accidents and occupational diseases are determined in advance. In addition to sound, image, health, location and environment data, physical parameters such as distance, level and pressure are monitored instantly with sensors. Managers are warned when a dangerous situation or behavior is detected in and threshold values are exceeded. In addition to employee and vehicle location tracking, predictive maintenance is provided by monitoring the performance of work and production vehicles. With the decrease in occupational accidents and diseases, occupational safety performance increases and costs decrease.

Keywords

• Algorithm
• Work Accident
• Occupational Disease
• Algoritma
• İş Kazası
• Meslek Hastalığı

Denetimli Makine Öğrenme Algoritmalarıyla İş Kazası ve Meslek Hastalıklarının Önlenmesi: Farklı Sektör Uygulamaları

Abstract

Özet- İş sağlığı ve güvenliği disiplini proaktif bir yöntemle iş kazalarını ve meslek hastalıklarını önleyen bir disiplinidir. Çalışan sağlığı için ülkelerin uluslararası sözleşmeler ve işverenlerin ulusal mevzuatta sorumlulukları bulunmaktadır. İşverenlerce risk değerlendirmesinin yapılması, iş güvenliği eğitimlerinin verilmesi, denetimlerin gerçekleştirilmesi, iş güvenliği uzmanı ve işyeri hekimi çalıştırılması ve ile ilgili tüm çalışmaların kayıt altına alınması zorunludur. Ülkelerde iş müfettişleri ile denetimler yapılmakta ve özel şirketler iş güvenliği hizmeti vermektedir. Ancak, işçi, malzeme, iş ekipmanı akışının çok hızlı ve fazla olduğu petrokimya, rafineri gibi büyük sanayi tesislerinde yetkililerin iş güvenliğini izlemesi zorlaşmaktadır. İşyeri kapasitesi, çalışan sayısı ve malzeme akışı arttıkça iş kazaları ve meslek hastalıklarının türü ve sayısı da artmaktadır. Yapay zekâ teknolojileri, bu takipleri kolaylaştırmaktadır. Bu makalenin amacı, iş kazaları ve meslek hastalıklarına neden olan etkenlerin proaktif şekilde denetimli makine öğrenme algoritmalarıyla önlenmesinin farklı sektörlerde araştırılmasıdır. Sciencedirect, scopus, googlescholar veri tabanları üzerinde liteartür taraması yapılmış, sektörlerde kullanılan algoritmalar incelenmiştir. Literatürdeki çalışmalar ve farklı sektörlerdeki uygulamalara göre, sensörlerle toplanıp bulut bilişimle saklanan veriler, daha önceden eğitilip test edilmiş ilgili denetimli makine öğrenmesi algoritmalarına beslenerek iş kazaları ve meslek hastalıklarına neden olan faktörler önceden belirlenebilmekte ve tahminler yapılabilmektedir. Ses, metin ve görüntü verilerinin yanı sıra sağlık, konum, ortam, mesafe, seviye ve basınç gibi fiziksel parametreler de sensörlerle anlık takip edilebilmektedir. Aşılan eşik değerlerde, tehlikeli bir durum veya davranış tespitinde yöneticiler uyarılmaktadır. Çalışan ve araç konum takibinin yanı sıra iş ve üretim araçlarının performansı da izlenerek öngörücü bakım sağlanabilmektedir. Azalan iş kazası ve meslek hastalıklarıyla iş güvenliği performansı artmakta, maliyetler de azalmaktadır.

Keywords

• Algoritma
• İş Kazası
• Meslek Hastalığı

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