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With the advance in Industry 4.0, smart industrial monitoring has been proposed to timely discover faults and defects in industrial processes. Steel is widely used in manufacturing equipment, and steel surface defect inspection is of great significance to the normal operation of steel equipment in manufacturing workshops. In steel defect inspection systems, industrial inspection robots generate images via scanning steel surface, and processors perform surface defect inspection algorithms on images. We focus on applying advanced object detection techniques to surface defect inspection algorithm for sheet steel. In the proposed steel surface defect inspection model, a deformable convolution enhanced backbone network firstly extracts complex features from multi-shape steel surface defects. Then the feature fusion network with balanced feature pyramid generates high-quality multi-resolution feature maps for the inspection of multi-size defects. Finally, detector network achieves the localization and classification of steel surface defects. The proposed model is evaluated on a typical steel surface defect dataset. Our model achieves 0.805 mAP, 0.144 higher than baseline models, and our model shows high efficiency in inference. Experiments are performed to reveal the effect of employed approaches, and results also show our model achieves a balance between inspection performance and inference efficiency.

Climate control systems are critical to ensuring the continuous operation of data centers, as they maintain the environmental conditions required by sensitive electronic equipment. In this context, continuous supervision of refrigeration compressors is essential to prevent failures that may compromise thermal stability. This work presents the design, implementation, and experimental validation of a remote monitoring and condition-based maintenance framework built on Industrial Internet of Things (IIoT) technologies for air-conditioning compressors used in data centers. The proposed architecture integrates industrial-grade sensors for temperature, electric current, and vibration, a Siemens LOGO! programmable logic controller (PLC) for signal acquisition and scaling, a Node-RED middleware layer for data flow management, and the ThingSpeak cloud platform for remote storage and analysis. The novel contributions of this work are: (i) a fully integrated low-cost IIoT stack validated on a Copeland ZR144KCE-TF5 scroll compressor under real operating conditions over a continuous 49-day monitoring period; (ii) a hybrid anomaly detection model that combines Z-score statistical baselines with moving-average prediction error to reduce false positives from transient events; and (iii) a condition-based maintenance decision framework that maps the three monitored variables to ISO 10816-3 vibration severity zones and manufacturer-referenced thermal and electrical thresholds, producing recommended maintenance actions. The framework was applied to the acquired dataset, confirming predominantly stable operation (93.4% of samples in ISO 10816-3 Zones A–B) while detecting an emergent mechanical-wear trend (5.64% of samples in Zone C) concentrated in the final days of the monitoring period and demonstrating the feasibility of the proposed architecture as a scalable and replicable solution for condition monitoring and maintenance decision support in critical technological infrastructures.

This work presents an ultrasound tomography imaging system and method for quantitative mapping of the sound speed in liquid masses. It is highly desirable to be able to inspect vessel fluid mass distribution, notably in the chemical and food industrial operations. Optimization of industrial reactors has been crucial to the improvement of industrial processes. There is a great need to investigate how and if tomographic imaging sensors could aid the automatic control of these process tanks. Single-measurement ultrasound techniques and especially spectrometric methods have been a subject of study of industrial applications. Tomographic systems provide key multi-dimensional and spatial information when compared to the well-established single-channel measurement system. Recently, ultrasound tomography has attracted a great deal of interest in a wide spectrum of industrial applications. The system has been designed as 32 piezoelectric ring-array positioned in a 30 cm tank, with an excitation frequency of 40 kHz. Two-dimensional transmission travel-time tomography was developed to reconstruct the fluid mass distributions. Prior experiments are mainly based on inclusions of a few centimetres and on a liquid solution of different concentrations. They have been conducted to test the spatial and quantitative resolution of the ultrasound imaging device. Analysing the reconstructed images, it is possible to provide accurate spatial resolution with low position errors. The system also demonstrated inclusion movement with a temporal resolution of 4 frames per second (fps) in dynamical imaging sense. Sound velocity quantitative imaging was developed for the investigation of ultrasonic propagation in different liquids. This work, for the first time, shows how quantitative sound velocity imaging using transmission mode time of flight data could be used to characterize liquid density distribution of industrial reactors. The results suggest that ultrasound tomography can be used to quantitatively monitor important process parameters.

Distributed Acoustic Sensing (DAS) systems face increasing challenges in massive data processing and real-time fault diagnosis due to the growing complexity of industrial environments and data volume. To address these issues, an end-to-end diagnostic framework is developed, integrating Mel-Frequency Cepstral Coefficients (MFCCs) for high-efficiency signal compression and Liquid Neural Networks (LNNs) for lightweight, real-time classification. The MFCC algorithm, originally used in speech processing, is adapted to extract key features from DAS vibration signals, achieving compression ratios of 60–100× without significant information loss. LNNs’ dynamic topology and sparse activation enable high accuracy with extremely low latency and minimal computational cost, making it highly suitable for edge deployment. The proposed framework was validated both in simulated environments and on a real-world conveyor belt system at Qinhuangdao Port, where it achieved 100% accuracy across four vibration modes over 14 weeks of operation. Comparative experiments show that LNNs outperform traditional models such as 1D-CNN and LSTMs in terms of accuracy, inference speed, and model size. The proposed MFCC-LNN pipeline also demonstrates strong cross-domain generalization capabilities in pipeline monitoring, seismic detection, and speech signal processing.

Reflectometry-based sensing systems are widely used in industrial monitoring to assess the condition of distributed assets such as cables and transmission lines. In practical sensing environments, however, correlation-based interpretation can become unreliable because of bilinear interference, dispersive propagation, and excitation mismatch, often producing artifact-related responses that lead to unnecessary inspections and reduced decision reliability. This paper proposes a temporal-consistency-based reliability enhancement framework for correlation-driven time–frequency domain reflectometry (TFDR). Instead of replacing the conventional reflectometry pipeline, the proposed method introduces a reliability-estimation layer that evaluates the trustworthiness of correlation responses and suppresses temporally inconsistent artifacts. Multiple complementary descriptors extracted from the reflected signal are jointly analyzed to determine whether a correlation response is propagation-consistent or more likely to arise from non-physical artifacts. Temporal consistency is modeled using a bidirectional long short-term memory (BiLSTM) architecture that captures long-range dependencies along the propagation sequence. Experimental results obtained from cable reflectometry measurements under varying impedance conditions show that the proposed framework effectively suppresses artifact-related correlation responses while preserving physically meaningful reflections required for fault localization. Additional cross-excitation evaluation provides preliminary evidence that the learned temporal-consistency criterion is not tightly coupled to a single excitation waveform. Because the proposed framework operates as a post-processing reliability layer, it can be integrated into existing reflectometry-based monitoring systems without the modification of the sensing hardware or excitation scheme.

Smart manufacturing relies on programmable logic controllers (PLCs) that translate sensor inputs into actuator commands. Generating PLC programs in legacy textual languages such as Mitsubishi FX-series Instruction List (IL) remains an expert-only task, and IL's deprecation in IEC 61131-3 Edition 3.0 leaves it under-represented in the corpora that train modern large language models (LLMs). We benchmark ten open-source LLMs (five vendors, 7B-122B parameters) in both LLM-only and Retrieval-Augmented Generation (RAG) configurations on a frozen 285-question dataset; the pipeline uses ChromaDB with all-MiniLM-L6-v2 embeddings and Maximal Marginal Relevance (MMR) retrieval (k=3, λ=0.5). To move beyond lexical similarity we introduce a three-tier static syntax checker (Lexical/Syntactic/Semantic) calibrated against a 93.3% ground-truth pass rate. RAG raises the syntactic pass rate by +6.7 to +61.1 percentage points across all ten models; the best configuration, qwen3.5:122b with RAG, reaches 95.8%, exceeding the ground-truth baseline. Two outliers (llama3.3:70b at +6.7 pp, gpt-oss:120b at +25.6 pp) are reported rather than excluded. The results indicate that for deprecated-but-deployed industrial languages a curated dialect corpus paired with a locally-hosted open-source LLM is more effective than scaling raw model size, supporting reproducible, on-premise industrial-monitoring and code-generation tooling for sustainable smart manufacturing.

Smart manufacturing, Industry 4.0, and cyber-physical systems (CPSs) require sensing architectures capable of resolving both spatially distributed asset behavior and highly localized process states. This review examines optical fiber sensors (OFSs) and integrated photonic sensors for industrial monitoring through a deployment-oriented, multi-scale perspective. The discussion covers five major application regimes: continuous infrastructure surveillance, structural health monitoring (SHM) of load-bearing composites, dynamic condition monitoring of machinery, in situ observability in advanced manufacturing, and localized chemical or gas sensing. Extended fiber-optic networks, including distributed fiber-optic sensing (DFOS) based on Rayleigh, Raman, and Brillouin scattering, together with multiplexed fiber Bragg grating (FBG) sensors, provide passive, embeddable, and remotely interrogated monitoring for large-scale assets and harsh environments. Photonic integrated circuits (PICs) shift transduction to compact node-level devices for localized thermal, mechanical, refractive-index, absorption, vibration, and inertial measurements, while plasmonic and dielectric nanophotonic sensors extend optical monitoring toward surface-selective and chemically specific detection. Across these platforms, digital signal processing (DSP), machine learning (ML), sensor fusion, and digital-twin (DT) coupling are treated as artificial-intelligence-enabled (AI-enabled) layers for signal recovery, inverse mapping, uncertainty reduction, and predictive maintenance. The review argues that scalable industrial adoption is less limited by sensing physics than by the complete deployment chain: packaging, fiber-chip interfacing, calibration stability, interrogation robustness, and AI-enabled data interpretation. This manuscript is structured as a deployment-oriented narrative review of optical fiber and integrated photonic sensors for industrial monitoring and smart manufacturing.

This study investigates the integration of soft sensors and deep learning in the oil-refinery industry to improve monitoring efficiency and predictive accuracy in complex industrial processes, particularly de-ethanization and debutanization. Soft sensor models were developed to estimate critical variables such as the C2 and C5 contents in liquefied petroleum gas (LPG) after distillation and the energy consumption of distillation columns. The refinery’s LPG purification process relies on periodic sampling and laboratory analysis to maintain product specifications. The models were tested using data from actual refinery operations, addressing challenges such as scalability and handling dirty data. Two deep learning models, an artificial neural network (ANN) soft sensor model and an ensemble random forest regressor (RFR) model, were developed. This study emphasizes model interpretability and the potential for real-time updating or online learning. The study also proposes a comprehensive, iterative solution for predicting and optimizing component concentrations within a dual-column distillation system, highlighting its high applicability and potential for replication in similar industrial scenarios.

This paper presents a systematic review of vibration sensors and their application in industrial-monitoring systems, aiming to provide a comprehensive reference for both academic research and practical applications in this field. Through the classification of measured parameters and sensing principles, this work endeavors to establish a structured understanding of vibration sensor’s working mechanism and deliver an in-depth analysis of their recent research achievements. By integrating practical cases from typical domains, this manuscript comprehensively demonstrates the practical value and application potential of vibration sensors in equipment-monitoring systems, illustrating how these sensors are utilized to detect mechanical failures and enhance the performance and safety of industrial systems, such as wind turbine, tunnel boring machine, and aerospace engine. Looking forward, with the rapid advancement of the Internet of Things (IoT) and artificial intelligence (AI) technologies, vibration sensors are anticipated to evolve towards multifunctionalization, miniaturization and intelligentization, thereby forming a comprehensive monitoring network that improves overall efficiency and reliability of the mechanical systems.

The application scenarios and market shares of industrial robots have been increasing in recent years, and with them comes a huge market and technical demand for industrial robot-monitoring system (IRMS). With the development of IoT and cloud computing technologies, industrial robot monitoring has entered the cloud computing era. However, the data of industrial robot-monitoring tasks have characteristics of large data volume and high information redundancy, and need to occupy a large amount of communication bandwidth in cloud computing architecture, so cloud-based IRMS has gradually become unable to meet its performance and cost requirements. Therefore, this work constructs edge–cloud architecture for the IRMS. The industrial robot-monitoring task will be executed in the form of workflow and the local monitor will allocate computing resources for the subtasks of the workflow by analyzing the current situation of the edge–cloud network. In this work, the allocation problem of industrial robot-monitoring workflow is modeled as a latency and cost bi-objective optimization problem, and its solution is based on the evolutionary algorithm of the heuristic improvement NSGA-II. The experimental results demonstrate that the proposed algorithm can find non-dominated solutions faster and be closer to the Pareto frontier of the problem. The monitor can select an effective solution in the Pareto frontier to meet the needs of the monitoring task.