Design of an intelligent hydraulic lash adjuster wear detection system based on machine vision

Noralahi, Zahra; Hashemi Daryan, Jafar; Sadat Akhavi, Seyed Mohammadreza; Mirzababayi Mostofi, Tohid

doi:10.22034/er.2026.2076349.1108

Design of an intelligent hydraulic lash adjuster wear detection system based on machine vision

Articles in Press

Document Type : Original Article

Authors

zahra noralahi

Jafar Hashemi Daryan

seyed mohammadreza sadat akhavi

Tohid Mirzababayi Mostofi

Ipco

10.22034/er.2026.2076349.1108

Abstract

Timely detection of failures in critical components of internal combustion engines is one of the key factors in extending engine lifetime and reducing repair and maintenance costs. In this study, an intelligent machine vision-based system was designed and implemented to detect failures (wear) in Hydraulic Lash Adjusters (HLA). The main objective of this system is to replace traditional and costly inspection methods, such as coordinate measuring machines, with a faster, more accurate, and cost-effective approach. The dataset consisted of color images of normal and defective parts. After a preprocessing stage including resizing, normalization, and data augmentation, Resnet18 was employed for Normal/Damaged classification, while a U-Net architecture was used for defect segmentation. The proposed system was evaluated using accuracy, recall, precision, and overlap-based metrics. Results demonstrated that the proposed model, compared with traditional methods, not only achieved higher accuracy (over 97.6% in classification and final Dice Score of 0.750) but also reduced inspection time by more than 70%. Furthermore, a comparison between the system’s output and expert human evaluation showed high consistency, confirming the reliability and practical applicability of the proposed method.

Keywords

Machine Vision

Classification

Image Segmentation

Internal Combustion Engine

Engine Parts Failure Detection

Subjects