Abstract
Abstract. Process sensor data allow for not only the control of industrial processes but also an assessment of plant conditions to detect fault conditions and wear by using sensor fusion and machine learning (ML). A fundamental problem is the data quality, which is limited, inter alia, by time synchronization problems. To examine the influence of time synchronization within a distributed sensor system on the prediction performance, a test bed for end-of-line tests, lifetime prediction, and condition monitoring of electromechanical cylinders is considered. The test bed drives the cylinder in a periodic cycle at maximum load, a 1 s period at constant drive speed is used to predict the remaining useful lifetime (RUL). The various sensors for vibration, force, etc. integrated into the test bed are sampled at rates between 10 kHz and 1 MHz. The sensor data are used to train a classification ML model to predict the RUL with a resolution of 1 % based on feature extraction, feature selection, and linear discriminant analysis (LDA) projection. In this contribution, artificial time shifts of up to 50 ms between individual sensors' cycles are introduced, and their influence on the performance of the RUL prediction is investigated. While the ML model achieves good results if no time shifts are introduced, we observed that applying the model trained with unmodified data only to data sets with time shifts results in very poor performance of the RUL prediction even for small time shifts of 0.1 ms. To achieve an acceptable performance also for time-shifted data and thus achieve a more robust model for application, different approaches were investigated. One approach is based on a modified feature extraction approach excluding the phase values after Fourier transformation; a second is based on extending the training data set by including artificially time-shifted data. This latter approach is thus similar to data augmentation used to improve training of neural networks.
Highlights
In the Industry 4.0 paradigm, industrial companies have to deal with several emerging challenges of which digitalization of the factory is one of the most important aspects for success
Data sets with time synchronization errors were considered to investigate their influence on results obtained with a machine learning (ML) software toolbox for condition monitoring and fault diagnosis
Minimal synchronization errors between the individual sensors, when already present in the training data, only have a small effect on the cross-validation error achieved with the ML toolbox
Summary
In the Industry 4.0 paradigm, industrial companies have to deal with several emerging challenges of which digitalization of the factory is one of the most important aspects for success. To make full use of the wideranging potential of smart sensors, the quality of sensor data has to be taken into account (Teh et al, 2020) This is limited by environmental factors, sensor failures, measurement uncertainty, and – especially in distributed sensor networks – by time synchronization errors between individual sensors. Dorst et al.: Influence of synchronization within a sensor network on machine learning results frastructure allowing for an assessment of the data quality In this contribution, a software toolbox for statistical machine learning (Schneider et al, 2017, 2018b; Dorst et al, 2021a) is used to evaluate large data sets from distributed sensor networks under the influence of artificially generated time shifts to simulate synchronization errors. Improved time synchronization might not be possible or be too costly, especially in existing sensor networks which were often never designed for sensor data fusion, so the ML approach can be improved to achieve a more robust model with acceptable results as demonstrated in this contribution
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