The process controller in a precision grinder for bearing rings puts high performance demands on the machine to achieve desired quality in production. This paper presents a unique approach of adding additional sensors for machine condition monitoring for the purpose of learning and using high fidelity condition indicators. The consolidation of real-time sensor data and the process control signals yields high-dimensional dataset. Automatic segmentation helps optimize the amount of data for processing and data mining ahead of fault diagnosis. The proposed setup is state of the art for prognostics as part of condition-based maintenance in a production machine.

Bearing ring grinding incorporates sensors to control the grinding cycle in real time. Prediction of output quality is difficult due to the complex combination of process settings and machine characteristics. Causal relationship of machine performance with varying operating conditions was studied with reference to the produced quality by adding condition monitoring setup to the machine. Data driven diagnostics of machine condition through integration of condition and process monitoring sensor data at the completion of the grinding cycle improves quality cognisance. This can be used to tune control parameters to achieve more predictable quality in successive cycles.

The demand on a bearing ring grinder, as any other machine tool, is to produce parts as per the specification and desired quality. A failure to achieve the quality can be due to functional issues or mechanical failure modes. Ultimately this results in lower productivity and higher production costs. Despite the increased emphasis on practicing condition-based machine maintenance (CBM) in manufacturing applications, it is still considered a challenge to fully deploy CBM in production machines due to diversity in equipment and variety in machine configurations as well as complex characteristics of failure modes. Although there exists extensive literature on CBM for machine tools and subsystems, the issue remains with realization of a technically capable and cost effective CBM system, specifically for a bearing ring grinder. Therefore, sensor(s) selection, data acquisition setup, data processing and analysis are the essential factors considered in the proposed framework to ensure a systematic and organized CBM implementation. The CBM setup is evaluated against production of bearing rings under different process and failure conditions. A machine type independent data acquisition system is designed to capture both machine and process dynamics. The data gathered from sensors at strategic locations exhibits its effectiveness in capturing the process and condition variations in relation to time and operating modes. The presented results of data analysis support the capability and effectiveness of the proposed framework. The utility of this framework can be extended for any number of scenarios including predictive maintenance or adaptive process optimization where solutions using machine learning and artificial intelligence tools can benefit from high dimensional structured dataset. The proposed framework provides a strong foundation to fast track the adaption of CBM in other production machines having similar subsystems.

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.

Grinding processes’ stochastic nature poses a challenge in predicting the quality of the resulting surfaces. Post-production measurements for form, surface roughness, and circumferential waviness are commonly performed due to infeasibility in measuring all quality parameters during the grinding operation. Therefore, it is challenging to diagnose the root cause of quality deviations in real-time resulting from variations in the machine’s operating condition. This paper introduces a novel approach to predict the overall quality of the individual parts. The grinder is equipped with sensors to implement condition-based maintenance and is induced with five frequently occurring failure conditions for the experimental test runs. The crucial quality parameters are measured for the produced parts. Fuzzy c-means (FCM) and Hotelling’s T-squared (T2) have been evaluated to generate quality labels from the multi-variate quality data. Benchmarked random forest regression models are trained using fault diagnosis feature set and quality labels. Quality labels from the T2 statistic of quality parameters are preferred over FCM approach for their repeatability. The model, trained from T2 labels achieves more than 94% accuracy when compared to the measured ring disposition. The predicted overall quality using the sensors’ feature set is compared against the threshold to reach a trustworthy maintenance decision.

In the manuscript, we have investigated the effective use of sensors in a bearing ring grinder for failure classification in the condition-based maintenance context. The proposed methodology combines domain knowledge of process monitoring and condition monitoring to successfully achieve failure mode prediction with high accuracy using only a few key sensors. This enables manufacturing equipment to take advantage of advanced data processing and machine learning techniques.

The grinding machine is of type SGB55 from Lidköping Machine Tools and is used to produce functional raceway surface of inner rings of type SKF-6210 deep groove ball bearing. Additional sensors like vibration, acoustic emission, force, and temperature sensors are installed to monitor machine condition while producing bearing components under different operating conditions. Data is sampled from sensors as well as the machine's numerical controller during operation. Selected parts are measured for the produced quality.

I publikationen har vi undersökt användningen av sensorer i en lagerringsslipmaskin för felklassificering och tillståndsövervakning. Föreslagen metod kombinerar domänkunskap om processövervakning och tillståndsövervakning för att framgångsrikt uppnå fellägesförutsägelse med hög noggrannhet med endast ett fåtal nyckelsensorer. Denna forskning visar att tillverkningsutrustning kan dra fördel av avancerad databehandling och maskininlärningsteknik.

Slipmaskinen är av typ SGB55 från Lidköping Machine Tools och används i detta fall för att slipa löpbanor på lagerinnerringar av typ SKF-6210 spårkullager. Sensorer för vibration, akustisk emission, kraft och temperatur är installerade för att övervaka maskinens tillstånd under slipning och olika driftsförhållanden. Data insamlas från sensorerna samt maskinens numeriska styrenhet under drift. Utvalda producerade kvalitetsparametrar mäts efter slipoperationen.