On-line Wear Debris Analysis (WDA) with its rapid fault detection capability in a non-destructive manner, has been increasingly employed in machine condition monitoring. However, the available debris features obtained from current on-line WDA processes are mostly restricted to statistical indicators due to the limited data quality. Morphological features extraction from individual debris, hence, has not been fully successful in practice. Moreover, wear debris detections are currently relying on single-side or two-dimensional (2-D) debris features, while the three-dimensional (3-D) features that contain valuable morphological information are still not accessible in on-line WDA and its further application is thus impeded. To extract more individual debris features in current on-line WDA, this research aims at formulating an efficient scheme with improved debris observation, robust features extraction and 3-D debris shape measurement. First, an automatic framework is established to collect debris information in multi-views by observing the moving debris in a flow cell. Since the debris profiles are captured when it is moving, motion blur inevitably occurs and degrades the image quality. A fusion based restoration method is then developed to remove the motion blur by integrating multiple deblurring processes over several localised Point Spread Function (PSF). Furthermore, out-of-focus blur often occurs as another predominant source of degradation that leads to low feature extraction accuracy. This problem is tackled here by utilising a Convolutional Neural Networks (CNN) to model the defocus process and then remove the degradation. Finally, given the improved debris profiles, a debris shape measurement procedure is constructed to estimate 3-D debris features by minimising the discrepancies between multiple potential reconstructions. Through validation experiments and comparisons, it indicates that the proposed 3-D measurement framework could enable the observed debris information to be extended into the third dimension that is rarely achieved by available on-line detectors for WDA. Compared with other debris imaging approaches, the developed method allows the estimation of material loss based on the measured debris volume. Furthermore, the classification of debris shape into chunk, laminar and sphere is now accomplishable with no specialised equipment. The industrial practicability of WDA can be improved considerably.