Degradation State Recognition of Rolling Bearing Based on K-Means and CNN Algorithm

Accurate degradation state recognition of rolling bearing is critical to effective condition based on maintenance for improving reliability and safety. In this work, a new architecture is proposed to recognize the degradation state of the rolling bearing. Firstly, the time-domain features including RMS, kurtosis, skewness and RMSEE, and Mel-frequency cepstral coefficients features are extracted from bearing vibration signals, which are then used as the input of k-means algorithm. These unlabeled features are clustered by k-means in order to define the different categories of the bearing degradation state. In this way, the original vibration signals can be labeled. Then, the convolutional neural network recognition model is built, which takes the bearing vibration signals as input, and outputs the degradation state category. So, interference brought by human factors can be eliminated, and further, the bearing degradation can be grasped so as to make maintenance plan in time. The proposed method was tested by bearing run-to-failure dataset provided by the Center for Intelligent Maintenance System, and the result proved the feasibility and reliability of the methodology.

Das intelligente Instandhaltungssystem/The intelligent maintenance system - An approach for the self-learning structuring of maintenance libraries

Der Fachbeitrag stellt eine Methode vor, mit der sich Bibliotheken von Instandhaltungsmaßnahmen selbstlernend gestalten lassen. Die „Intelligenz“ solcher Systeme bietet mehrfachen Nutzen, einerseits durch die Auswahl der passenden Instandhaltungsmethode zum richtigen Zeitpunkt, andererseits durch die damit verbundene Erhöhung des kompletten Abnutzungsvorrates. Die Ergebnisse sind im Sonderforschungsbereich 653 „Gentelligente Bauteile im Lebenszyklus – Nutzung vererbbarer, bauteilinhärenter Informationen in der Produktionstechnik“ entstanden.   This article describes a method to design a self-learning maintenance library. The benefit derived from the intelligence of those systems refers to the right choice of maintenance measures at the right time and the enhancement of the whole wear margin. The results are part of the Collaborative Research Centre 653: Gentelligent components in their lifecycle – Utilization of inheritable component information in product engineering.