Czasopismo | POLISH TECHNICAL REVIEW | Rocznik 2024 - zeszyt 4

ENERGY-EFFICIENT DETECTION OF RAILWAY WHEEL DEFECTS USING HILBERT TRANSFORM-BASED VIBRATION ANALYSIS AND HISTOGRAM CLUSTERING

ENERGOOSZCZĘDNE WYKRYWANIE USZKODZEŃ KÓŁ KOLEJOWYCH PRZY UŻYCIU ANALIZY DRGAŃ OPARTEJ NA TRANSFORMACIE HILBERTA I KLASTERYZACJI HISTOGRAMÓW

10.15199/180.2024.4.6

nr katalogowy: 152249
10.15199/180.2024.4.6

Streszczenie

Zapewnienie bezpieczeństwa i niezawodności systemów transportu kolejowego w dużym stopniu zależy od wczesnego wykrywania i monitorowania usterek zestawów kołowych. Jeśli nie zostaną sprawdzone, niedoskonałości, takie jak płaskie miejsca na kołach, mogą zwiększyć poziom drgań, przyspieszyć zmęczenie podzespołów i doprowadzić do poważnych incydentów bezpieczeństwa. Tradycyjne podejścia do diagnozowania stanu kół często opierają się na ręcznych inspekcjach lub sprzęcie wymagającym dużych zasobów, co może być trudne do wdrożenia w dużych flotach lub sieciach zdalnych. Artykuł prezentuje energooszczędną metodę wykrywania defektów kół kolejowych przy użyciu analizy wibracji. Wykorzystano transformację Hilberta do przetwarzania sygnałów oraz klasteryzację histogramową w celu identyfikacji nieprawidłowości. Rozwiązanie to jest bardziej efektywne energetycznie i precyzyjne w porównaniu z tradycyjnymi metodami. Skupiono się na analizie drgań jako wskaźnika stanu technicznego kół, co ma istotne znaczenie dla bezpieczeństwa i utrzymania infrastruktury kolejowej. Wyniki pokazują, że poprzez skupienie się na konkretach i wykorzystanie prostych, ale skutecznych analiz statystycznych, system może niezawodnie odróżniać sprawne koła od wadliwych. Przyczynia się to do opłacalnych strategii konserwacji, skraca przestoje i zwiększa bezpieczeństwo kolei. Na koniec omawiamy przyszłe ulepszenia, w tym integrację z ramami konserwacji predykcyjnej, adaptacyjne techniki progowe i potencjalne włączenie bardziej zaawansowanych metod przetwarzania, jeśli pozwolą na to budżety energetyczne.

Abstract

Ensuring the safety and reliability of railway transport systems depends heavily on the early detection and monitoring of wheelset defects. If left unchecked, imperfections such as flat spots on wheels can increase vibration levels, accelerate component fatigue, and lead to serious safety incidents Traditional approaches to diagnosing wheel condition often depend on manual inspections or resource-intensive equipment, which can be challenging to deploy across large fleets or remote networks. This paper presents an energy-efficient, computationally lean method for detecting characteristic vibration patterns associated with common wheelset defects. The proposed approach employs the Hilbert transform to extract specific dynamic responses in time domain of vibration signals of vibration signals and then utilizes histogram-based clustering to identify deviations in peak distribution that indicate mechanical defects. Unlike more complex machine learning approaches that demand significant power and computational resources, our method is designed to operate autonomously on low-power, self-contained devices with limited external support. It achieves robust performance in a vehicle speed range of 10–40 km/h and track conditions, detecting subtle anomalies after accumulating modest amounts of data from short measurement windows. The results show that by focusing on the specific and exploiting simple but effective statistical analyses, the system can reliably differentiate healthy from defective wheels. This contributes to cost-effective maintenance strategies, reduces downtime, and enhances railway safety. Finally, we discuss future improvements, including integration with predictive maintenance frameworks, adaptive thresholding techniques, and potentially incorporating more advanced processing methods if energy budgets allow.

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Keywords

Bibliografia

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Open Access

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2024-4

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Open Access

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