An accurate prediction of complex product quality parameters from process time series by an end-to-end learning approach remains a significant challenge in machine learning. A special difficulty is the application of industrial batch process data because many batch processes generate variable length time series. In the industrial application of such methods, explainability is often desired. In this study, a 1D convolutional neural network (CNN) algorithm with a masking layer is proposed to solve the problem for time series of variable length. In addition, a novel combination of 1D CNN and class activation mapping (CAM) technique is part of this study to better understand the model results and highlight some regions of interest in the time series. As a comparative state-of-the-art unsupervised machine learning method, the One-Nearest Neighbours (1NN) algorithm combined with dynamic time warping (DTW) was used. Both methods are investigated as end-to-end learning methods with balanced and unbalanced class distributions and with scaled and unscaled input data, respectively. The FastDTW and DTAIDistance algorithms were investigated for the DTW calculation. The data set is made up of sensor signals that was collected during the production of plastic parts. The objective was to predict a quality parameter of plastic parts during production. For this research, the quality parameter will be a difficult or only destructively measurable parameter and both methods will be investigated for their applicability to this prediction task. The application of the proposed approach to an industrial facility for producing plastic products shows a prediction accuracy of 83.7%. It can improve the reverence method by approximately 1.4%. In addition to the slight increase in accuracy, the CNN training time was significantly reduced compared to the DTW calculation.

This article provides insight into the development of a powerful and low-cost chopper amplifier for piezoelectric pressure sensors and shows its possible applications for injection moulding machines. With a power supply of 3.3 volts and the use of standard components, a circuit is introduced which can be connected to a commercially available microcontroller without any additional effort. This amplifier is specialised for low frequencies and high-pressure environments. With the adjustment of the sample and chopper frequency by means of software, the amplifier can easily be adapted for other applications. This chopper amplifier is a very compact and cost-effective solution with a small number of required components. In this contribution, it will be shown that the amplifier has good results in various laboratory tests as well as in the production process. Furthermore, an approach to fuse data from force and pressure signals by using a Kalman filter will be presented. With this method, the quality of the sensor signals can be significantly improved. This article is an extension of our previous work in Schneider et al. (2016b).

Detecting rare structural damage without labeled fault data remains a critical unsolved challenge in structural health monitoring (SHM). Prestressed concrete catenary poles are key elements of high-speed railway infrastructure, and undetected degradation can compromise safety and service reliability. This paper introduces BcDCGAN, a Bayesian conditional deep convolutional generative adversarial network designed for unsupervised anomaly detection in multivariate vibration time series from three in-service catenary poles. Trained exclusively on healthy acceleration signals with wind-speed conditioning, the model learns the normal structural dynamics and produces an uncertainty-based anomaly score that combines reconstruction quality, adversarial evaluation, and epistemic uncertainty into a single decision function. An adaptive, data-driven threshold estimate from healthy validation data enables practical deployment without damage labels. On a real 2017 catenary pole dataset (1606 signals, 70/10/20 split) with injected, physically motivated damage-like patterns, BcDCGAN achieves high anomaly recall with interpretable uncertainty signals and clear separation between normal and anomalous latent representations. The results suggest that Bayesian conditional GANs can support risk-aware monitoring of railway infrastructure under varying environmental and operational conditions.

Detecting rare structural damage without labeled fault data remains a critical unsolved challenge in structural health monitoring (SHM). This paper introduces BcDCGAN, a Bayesian conditional deep convolutional generative adversarial network designed for unsupervised anomaly detection in multivariate vibration time series from prestressed concrete catenary poles. The architecture integrates variational Bayesian inference over generator and critic weights with temporal convolutional networks, enabling epistemic uncertainty alongside reconstruction and critic objectives. Trained exclusively on healthy acceleration signals with wind speed conditioning, the model produces a log-space Bayesian anomaly score that jointly combines normalized reconstruction error, critic evaluation, and epistemic uncertainty estimates into a single weighted decision function. An adaptive threshold is calibrated from the validation data for deployment-ready performance. Evaluation on a real 2017 catenary pole dataset (1606 signals, 70/10/20 split) with injected anomalies achieves 99.2% recall while revealing clear latent space separation and appropriate uncertainty signaling for out-of-distribution samples. Progressive posterior uncertainty reduction during training confirms robust learning of healthy structural dynamics, supporting interpretable, risk-aware decisions in safety-critical railway infrastructure.