The needle assembly mixed anomaly ray image detection method for needle roller bearings based on double threshold segmentation

Abstract Needle roller bearings without cages are extensively utilized in the mechanical transmission systems of various equipment due to their high load-carrying capacity and compact design. However, due to the limited performance of the current needle loading equipment, abnormalities such as missing needles, skewed needles and toppling needles. If abnormal bearings are installed in the equipment above, it will make the equipment appear noisy, vibration is obvious, the smoothness of operation is reduced. To address this issue, this paper proposes a systematic detection method based on double-threshold segmentation for identifying mixed needle assembly anomalies. Initially, starting from the principles of ray detection, we analyze the characteristics of ray imaging for different needle assembly anomalies in needle roller bearings. Image samples of single and mixed needle assembly anomalies are collected using a ray detection platform. Subsequently, a dual-threshold segmentation method is introduced for mixed needle assembly abnormality images by combining different regions of the needle-bearing ray image and the grey value characteristics of the anomalies for threshold selection. A step-by-step detection method based on dual-threshold segmentation is then proposed. This method utilizes a mask to extract the non-needle assembly region in the high-threshold segmented image, achieving identification and quantification of skewed and toppling needles based on area threshold information of the abnormal needle roller contour. Additionally, a ring mask is employed to extract the needle roller gap in the low-threshold segmented image, allowing for the screening of the needle gap contour based on the area threshold, thus identifying and quantifying missing needle anomalies. Finally, experimental verification is conducted on various needle assembly anomaly images, demonstrating that the proposed method can effectively identify and quantify different needle assembly anomalies in needle roller bearings, offering valuable application insights for quality inspection during needle assembly processes.