Development and testing of a combined machine and process health monitoring system

Process monitoring has been shown to be capable of observing the quality of a machining operation through sensor signals and analysis in both the literature and in commercially available systems. Some of these systems provide an additional benefit of monitoring the health of a machine tool. However, the commercially available systems tend to utilise relatively simple analysis techniques for both the process and machine health, limiting their application and robustness. Industrial interest in systems that can profit from the current advances in machine tool digitalisation and data analytics has grown considerably. This is especially true for the capability of early-detection of quality issues in components, whilst also ensuring machine tools are in a condition that can achieve high quality production. The present research includes the development and testing of a fingerprint routine which can be run at regular intervals to detect potential failure modes or machine tool degradation through signal analysis. Machining trials were carried out with the objective of detecting known defects in a workpiece through signal analysis. For both cases, a combined monitoring system was developed for data capture during testing, and a number of failure modes and defects were physically simulated to test the possibility of detection in the acquired signals. Time domain, frequency domain, and time-frequency domain signal processing techniques were applied to the sensor data with various levels of success. Continuous wavelet transforms (CWT) were of particular interest, as they successfully captured signal changes between tests for the physically simulated failure modes of the machine tool and the component. Therefore, a comparative CWT analysis was developed which successfully emphasised some of the machine tool failure modes and part defects when compared to baseline signals. The output of the comparative analysis may be well-suited to automation through machine learning techniques.