Failure mode classification for condition-based maintenance in a bearing ring grinding machine

Abstract

Technical failures in machines are major sources of unplanned downtime in any production and result in reduced efficiency and system reliability. Despite the well-established potential of Machine Learning techniques in condition-based maintenance (CBM), the lack of access to failure data in production machines has limited the development of a holistic approach to address machine-level CBM. This paper presents a practical approach for failure mode prediction using multiple sensors installed in a bearing ring grinder for process control as well as condition monitoring. Bearing rings are produced in a set of 7 experimental runs, including 5 frequently occurring production failures in the critical subsystems. An advanced data acquisition setup, implemented for CBM in the grinder, is used to capture information about each individual grinding cycle. The dataset is pre-processed and segmented into grinding cycle stages before time and frequency domain feature extraction. A sensor ranking algorithm is proposed to optimize feature selection for failure classification and the installation cost. Random forest models, benchmarked as best performing classifiers, are trained in a two-step classification framework. The presence of failure mode is predicted in the first step and the failure mode type is identified in the second step using the same feature set. Defining the feature set in the failure detection step improves the predictor generalization with the classifiers’ performance accuracy of $$99\%$$ 99 % on the test dataset. The presented approach demonstrates an efficient failure mode classification by selecting crucial sensors resulting in a cost-effective CBM implementation in a bearing ring grinder.

Impact of Technological System’s Characteristics on the Machining Accuracy of Bearing Rings

“…The cut of the workpiece was measured at 12 points on the outer diameter (Figure 4). The roundness of the treated surface was up to 0.15 mm [15]. The obtained values of deviations are within the tolerance and, in some cases, significantly smaller than those provided on the control chart (Table 1).…”

A Novel Methodology for Measuring Ambient Thermal Effects on Machine Tools

“…The NCA distinguishes itself as an effective method for analysing relationships among a set of variables relative to a target variable and calculating their correlation indices. This capability makes the NCA particularly valuable in sensor networks for the purposes of sensor ranking and selection [36]. In the context of this study, an NCA was applied to explore the relationships between various temperature measurements and the ambient temperature, identifying those measurements that demonstrate the strongest correlation with the ambient temperature.…”

Advancing Offshore Oil and Gas Facilities: A Comprehensive Review of Innovative Maintenance Strategies for Enhanced Reliability and Efficiency

“…By addressing issues proactively, CBM enhances asset reliability and minimizes the risk of unexpected breakdowns (Alrabghi and Tiwari, 2015). addressing maintenance needs precisely when required, CBM reduces downtime and enhances operational efficiency (Ahmer et al, 2022).…”