To strengthen the economic pillar in sustainability assessment, the indicator ‘domestic value added’ is introduced. It aims at comparing established and less developed technologies regarding their prospective value added in a country. This is done by classifying a technology’s value added to the developed categories: domestic, potential domestic and non-domestic. Within this paper, two methods for assessing this indicator are introduced focussing on their applicability in a sustainability assessment context. Both methods are tested on a case study comparing two alternative drivetrain technologies for the passenger car sector (battery and fuel cell electric vehicle) to the conventionally used internal combustion engine. The first method is life cycle cost-based whereas the second is based on Input Output analysis. If a life cycle cost assessment is already available for the technology under assessment, the easier to implement life cycle cost-based approach is recommended, as the results are similar to the more complex Input Output-based approach. From the ‘domestic value added’ perspective, the battery electric vehicle is already more advantageous than the conventional internal combustion engine over the lifecycle. Fuel cell electric vehicles have the highest potential to increase their ‘domestic value added’ share in the future. This paper broadens the economic pillar in sustainability assessment by introducing a new indicator ‘domestic value added’ and giving practical information on how to prospectively assess it for existing and less developed technologies or innovations. Graphical abstract

Reducing greenhouse gas (GHG) emissions in the transport sector is one of the biggest challenges in the German energy transition. Furthermore, sustainable development does not stop with reducing GHG emissions. Other environmental, social and economic aspects should not be neglected. Thus, here a comprehensive sustainability assessment for passenger vehicles is conducted for 2020 and 2050. The discussed options are an internal combustion engine vehicle (ICEV) fuelled with synthetic biofuel and fossil gasoline, a battery electric vehicle (BEV) with electricity from wind power and electricity mix Germany and a fuel cell electric vehicle (FCEV) with hydrogen from wind power. The life cycle-based assessment entails 13 environmental indicators, one economic and one social indicator. For integrated consideration of the different indicators, the MCDA method Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is chosen. For the assessment, a consistent assessment framework, i.e. background scenario and system boundaries, and a detailed modelling of vehicle production, fuel supply and vehicle use are the cornerstones. The BEV with wind power is the most sustainable option in 2020 as well as in 2050. While in 2020, the second rank is taken by the ICEV with synthetic biofuel from straw and the last rank by the FCEV, in 2050 the FCEV is the runner-up. With the help of MCDA, transparent and structured guidance for decision makers in terms of sustainability assessment of motorized transport options is provided. Graphical abstract

White etching crack (WEC) failure is a failure mode that affects bearings in many applications, including wind turbine gearboxes, where it results in high, unplanned maintenance costs. WEC failure is unpredictable as of now, and its root causes are not yet fully understood. While WECs were produced under controlled conditions in several investigations in the past, converging the findings from the different combinations of factors that led to WECs in different experiments remains a challenge. This challenge is tackled in this paper using machine learning (ML) models that are capable of capturing patterns in high-dimensional data belonging to several experiments in order to identify influential variables to the risk of WECs. Three different ML models were designed and applied to a dataset containing roughly 700 high- and low-risk oil compositions to identify the constituting chemical compounds that make a given oil composition high-risk with respect to WECs. This includes the first application of a purpose-built neural network-based feature selection method. Out of 21 compounds, eight were identified as influential by models based on random forest and artificial neural networks. Association rules were also mined from the data to investigate the relationship between compound combinations and WEC risk, leading to results supporting those of previous analyses. In addition, the identified compound with the highest influence was proved in a separate investigation involving physical tests to be of high WEC risk. The presented methods can be applied to other experimental data where a high number of measured variables potentially influence a certain outcome and where there is a need to identify variables with the highest influence.

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White Etching Cracks (WEC) in gearbox bearings is a major concern in the wind turbine industry, which can lead to a premature failure of the gearbox. Though many hypotheses regarding the generation of WEC have been proposed over the decades, the answer is still disputable. To trace back the failures to earlier stages before they occur, an innovative sensor-set has been utilized on a test rig to monitor the influencing factors that lead to WEC. This paperwork seeks to recognize abnormal patterns from recorded sensor data and derive statements of sensible sensor combinations in WEC early detection. A Long Short Term Memory (LSTM) network-based autoencoder is proposed for the anomaly detection (AD) task. Employing an auto-associative sequence-to-sequence predictor, a model is trained to reconstruct the normal time series data without WEC. The reconstruction error of testing time series data is evaluated for the determination of its anomaly. The results show that the specified LSTM autoencoder framework can qualitatively distinguish anomalies from collected multivariate time series data. Moreover, the anomaly score evaluated via reconstruction-error-based metrics can discriminate normal and abnormal behaviors in the study. This investigation’s results entail a significant step towards early WEC risk detection and more cost-efficient wind turbine technology if this approach can be further applied on stream data with plausible thresholds in monitoring system.