Reduced Data Volumes through Hybrid Machine Learning Compared to Conventional Machine Learning Demonstrated on Bearing Fault Classification

n some real-world problems, machine learning is faced with little data due to limited resources such as sensors, time, and budget. In this case, the conventional machine learning approach may fail or perform badly. To develop a well-functioning model with a small training set the hybrid machine learning approach, the combination of different methods can be applied. Especially in the machine industry where Industry 4.0 is one of the most important topics—including condition monitoring, predictive maintenance, and automated data analyses—data are limited and costly. In this work, the conventional and hybrid approach are compared to the application of ball bearing fault classification. The dataset contains 12 different classes (11 with faults and 1 undamaged). For each approach, two different LSTM (Long Short-Term Memory) models are developed and trained on various training sets (different sensors). The hybrid model is realised by adding physical knowledge through applying fast Fourier transformation and frequency selection to the raw data. This study shows that the additional physical knowledge in the hybrid model results in a better performance of the hybrid machine learning than the conventional

Mit neuem Recyclingverfahren mehr aus Lithium-Ionen-Batterien herausholen

Die Berner Fachhochschule BFH entwickelt zusammen mit Partnern ein neues Verfahren für ein effizienteres Recycling von Lithium-Ionen-Batterien. Ein Ziel des Projekts besteht darin, mehr wertvolle Rohstoffe aus Altbatterien zurückzugewinnen und dadurch Materialkreisläufe zu schliessen

Laser Patterning of High‐Mass‐Loading Graphite Anodes for High‐​Performance Li‐​Ion Batteries

Given the ongoing efforts to build Li-ion batteries with higher volumetric energy and power densities, the research on enhancing Li-ion transport within compressed high-mass-loading electrodes at fast cycling conditions is imperative. In this work, we show that the rate capability of graphite electrodes with high areal capacity of 4.5 mAh cm2 and density of 1.79 g cm3 (15% of porosity) can be considerably improved by laser patterning, namely by the fabrication of arrays of vertically aligned channels serving as diffusion paths for rapid Li-ion transport. Resultant laser patterned graphite electrodes delivered enhanced volumetric capacity as compared to that of non-patterned electrodes (450 vs. 396 mAh cm3 at C/2 rate). The reduction of the total steady-state concentration drop within the graphite electrodes after their patterning was also assessed

Zustandsüberwachung von Mühlenpanzerungen durch maschinelles Lernen

Um eine übermäßige Abnutzung der Trommeln von Erzmühlen zu verhindern, werden diese mit austauschbaren Panzerungen ausgestattet. ABB und die Berner Fachhochschule haben ein Überwachungssystem für Mühlenpanzerungen entwickelt, das mithilfe von Beschleunigungssensoren und maschinellen Lernverfahren den besten Zeitpunkt zum Austausch der Panzerung bestimmt und somit Stillstandskosten reduziert