Physics-informed machine learning has shown strong potential in improving generalisation under limited or noisy data, but most existing approaches treat physical priors only as soft regularisation terms on the loss.This work introduces a physics-structured recurrent architecture where thermodynamic differential equations are embedded directly into LSTM state updates.Adaptive physical parameters are learned through auxiliary multilayer perceptrons, forming a differentiable hybrid dynamical system that fuses physics priors with sequence learning.Experiments on industrial datasets show improved robustness under unseen fault conditions, outperforming conventional LSTMs and PINN-style models.The framework offers a scalable and generalizable approach to physics-aware recurrent modeling.

Deploying deep learning models on resource-constrained hardware like Field-Programmable Gate Arrays (FPGAs) remains challenging despite advancements in quantization techniques, which often fail to map optimally to target hardware. This study proposes an end-to-end workflow for fixed-point quantization targeting FPGA accelerators, integrating hardware emulation within quantization-aware training to bridge software-hardware co-design. This ensures quantized models are optimized for real-world deployment. Evaluations on CIFAR-10 and ImageNet datasets using ResNet and VGG models show competitive performance, with up to 2% improvement in accuracy over state-of-the-art methods. This work provides insights to resolve the discrepancies that often occur between software-based quantization and hardware deployment. Our methodology effectively bridges quantization and deployment, providing a practical solution for edge device applications.

Construction equipment has important roles in industries such as construction and mining. Any downtime because of failures increase cost. Traditional diagnostic systems detect failures only after they occur, making it difficult to take precautions and prolonging repair times. This paper is the first to address the analysis of machine learning-powered Prognostic and Health Management (PHM) systems specifically for predicting failures in diesel engine air intake systems, focusing on two common issues: air leakage and Exhaust Gas Recirculation (EGR) blockage. This study compares various machine learning and deep learning models for anomaly detection and fault classification using real-world sensor data from controlled engine tests. The results demonstrate that ensemble and neural network-based machine learning methods, such as Random Forest, XGBoost, and LSTM, achieve highly successful predictions for anomaly detection and fault classification.

Industrial controllers constitute the core of numerous automation solutions. Continuous control system operation is crucial in certain sectors, where hardware duplication serves as a strategy to mitigate the risk of unexpected operational halts due to hardware failures. Standby controller redundancy is a commonly adopted strategy for process automation. This approach involves an active primary controller managing the process while a passive backup is on standby, ready to resume control should the primary fail. Typically, redundant controllers are paired with redundant networks and devices to eliminate any single points of failure. The process automation domain is on the brink of a paradigm shift towards greater interconnectivity and interoperability. OPC UA is emerging as the standard that will facilitate this shift, with OPC UA PubSub as the communication standard for cyclic real-time data exchange. Our work investigates standby redundancy using OPC UA PubSub, analyzing a system with redundant controllers and devices in publisher-subscriber roles. The analysis reveals that failovers are not subscriber-transparent without synchronized publisher states. We discuss solutions and experimentally validate an internal stack state synchronization alternative.

Control systems are often an integral part of automation solutions where high reliability is crucial due to the high cost of downtime. The risk of unplanned downtime is typically reduced with redundant solutions. Additionally, safety-critical automation functions require high-integrity controllers. Today, the prevalent redundancy solution is a standby scheme, where one active primary controller drives the process while a standby backup controller is ready to take over in case of primary failure. This redundant controller pair can consist of high - integrity controllers. The automation industry is trending towards Ethernet as the sole communication medium. Our work presents an initial study of a high-integrity realization of a redundancy failure detection mechanism that guarantees only one primary controller, even in the case of network partitioning between the redundant controller pair. The failure detection is a lease-based function that leases the primary role from a central lease broker. This work discusses a high-integrity realization of the primary redundancy role leasing. We deduce and present the high-integrity-related requirements and a high-level design as an initial step towards a high-integrity realization of the redundancy role leasing.

In this paper, we consider the mapping of two radar algorithms on a new scalable hardware architecture. The architecture consists of several computational modules that work independently and send data simultaneously in order to achieve high throughput. Each computational module is composed of multiple processors connected in a hypercube topology to meet scalability and high bisection bandwidth requirements. Free-space optical interconnects and planar packaging technology make it possible to transform the hypercubes into planes. Optical fan-out reduces the number of optical transmitters and thus the hardware cost. Two example systems are analyzed and mapped onto the architecture. One 64-channel airborne radar system with a sustained computational load of more than 1.6 TFLOPS, and one ground-based 128-channel radar system with extreme inter-processor communication demands. 1