arXiv subjects

John Burtenshaw

Publications and source records attributed to John Burtenshaw.

3 recordsLinked to original sources

arXiv2026

A Kafka-Centric Communication Fabric for Near-Real-Time, Cloud-Replicated Closed-Loop Manufacturing Process Control

Smart manufacturing needs to move sensor data off the plant floor, react to it, and feed decisions back to actuators within bounded time. Programmable logic controllers (PLCs) handle fast, deterministic, safety-critical actuation, but they are not designed for the higher-level functions required by Industry 4.0, such as predictive maintenance, machine learning inference, and cross-facility analytics. These functions need a scalable, durable, and observable communication substrate. We present the communication architecture of a production system that provides this substrate and closes the loop back to the plant in near real time. The design is built from industry-standard components: Apache Kafka as the streaming backbone, OPC-UA for PLC connectivity, a relational time-series database for persistence, and JSON for serialization. The novelty is architectural. We show how these standards are integrated for closed-loop industrial control through four design choices: a single event stream per production line serves control, monitoring, machine learning, and durable recording, allowing one producer to serve many independent consumers; a protocol bridge converts polled OPC-UA traffic into publish/subscribe streams, aligns per-signal timestamps to a common time base to remove cross-signal jitter, and provides a symmetric actuation path; a transport technique carries sub-second process dynamics at a coarser publication cadence by packing timestamped samples into fixed-order arrays; and an edge-to-cloud replication scheme keeps the edge authoritative, so local control continues during wide-area network outages while cloud analytics operate on replicated data. We describe the loop latency budget, report measured broker transport latency, and discuss operational experience. The system provides soft, near-real-time behavior rather than hard real-time guarantees.

cs.NI↗

arXiv2026

Augmenting PID Control with Deep Reinforcement Learning: A Hybrid Approach to the Industrial Benchmark

As industrial processes grow in complexity, traditional Proportional-Integral-Derivative (PID) controllers are often insufficient for handling their non-linear, multi-input dynamics. We propose using advanced Deep Reinforcement Learning (DRL) to prove its advantages in these complex environments. To do this, we rely on the Industrial Benchmark (IB). The IB is a realistic simulation that tests DRL algorithms against the key challenges of industrial applications: high-dimensional state spaces, delayed effects, and conflicting multi-criterial objectives. This testbed highlights DRL's core trade-off: while its final policies can often be unstable, its unique strength is the ability to autonomously discover optimal, non-obvious policies in multi-dimensional spaces where simple controllers fail. In this paper, we propose a novel hybrid PID-RL controller that leverages DRL's discovery capability while ensuring Reliability. After developing a multi-objective reward function to make DRL viable, we use a twin-delayed deep deterministic (TD3) agent as a discovery tool to find the optimal, non-obvious settings for the IB's 'Gain' and 'Shift' parameters. By feeding these discovered parameters to a simple, tuned PID controller, our hybrid model successfully combines all three characteristics: it achieves the optimal Performance and Efficiency of the best DRL agent with the Reliability of a classical controller. This work demonstrates a practical methodology for using DRL to augment, rather than replace, trusted industrial control systems.

cs.LG↗

arXiv2026

Benchmarking Hybrid Deep Learning Architectures for Predictive Maintenance in Industry 4.0

Predictive maintenance in Industry 4.0 refers to using data from sensors, machines, and production systems to estimate when equipment is likely to fail, so maintenance can be planned before a breakdown occurs [1]. However, a model that predicts maintenance may work perfectly in the lab but fail unexpectedly when applied to real factory data [2]. To solve this "reliability" gap, we evaluated six deep learning architectures across more than 700 experimental runs. We focused on the two dominant approaches in the field: Recurrent Neural Networks (RNNs), which process data step-by-step, like reading a sentence [3], and Transformers, a recent dominant approach, which look at the entire sequence at once to spot important connections [4]. We examined whether Transformers still outperform recurrent neural networks (RNNs) when the data includes noise [5]. We found that while Transformers excelled at tracking stable, slow-moving processes, they tend to overreact to chaotic data, mistakenly taking sensor noise for meaningful signals [6]. We also found that the hybrid method that combines a Long Short-Term Memory (LSTM) layer with a Transformer layer is more resilient to noisy data from factory shops [7]. Functioning as a noise filter, the LSTM smooths out data volatility, allowing the Transformer to focus on the bigger picture without being distracted [8]. The hybrid model did not just improve accuracy; it proved to be significantly more consistent than complex models, delivering reliable predictions regardless of how chaotic the underlying system became.

cs.LG↗