Automatic fault detection and diagnosis (FDD) are critical for maintaining reliable and efficient industrial systems. However, conventional methods rely heavily on manual inspections or threshold-based techniques, which often fail to capture the dynamic patterns in time series (TS) sensor data. As a result, faults persist for extended periods, leading to suboptimal system operations, increased energy waste, and significant economic losses. This work proposes a cross-modal framework that facilitates the efficient deployment of state-of-the-art pretrained vision models for enhanced FDD, with two novel TS-to-image transformations: first, an adapter deep encoder that learns optimal, task-specific representations from raw sensor data while generating outputs that are input-compliant with pretrained models. Second, an enhanced line plot that creates geometric shapes of two related signals. Comparative experiments against fixed methods, including spectrograms, Gramian angular fields, Markov transition fields, recurrence plots, and five deep learning baseline models, showed substantial performance gains across diverse domains. InceptionTime achieved the highest average baseline performance with an F<inf>1</inf> of 88.6%, while the adapter and shapes achieved 94.4% and 92.4%, respectively. The findings highlight the potential of the cross-modal framework for FDD to facilitate early intervention and efficient system maintenance in industrial settings.

Fault detection in district heating (DH) substations is critical for energy efficiency and reliability. However, it is challenged by scarce fault labels, low-frequency data, privacy concerns, and battery-constrained gateways. We propose a novel hybrid semi-supervised federated domain adaptation architecture for fault detection in DH. We use a one-class variational autoencoder (VAE) to leverage heterogeneous sensor streams from 434 distributed substations. First, we perform cross-network unsupervised pre-training on multi-sourced data from two independent real-world DH networks, fusing their return temperature dynamics into a robust shared manifold. Second, we leverage maintenance metadata to selectively allow verified-normal clients for per-round fine-tuning of the model. Third, we drastically reduce uplink costs by compressing each client's weight delta using 10% top-k sparsification and demonstrate that our pipeline enables robust few-shot finetuning with 20% of the normal operational data while retaining high detection performance. By strategically training, our method achieves F1 and G-mean scores of up to 97% and an AUC ≥ 99% on real-world DH data. To our knowledge, this is the first work to study cross-domain data fusion in the DH field for fault detection, aiming to enhance and enable effective, scalable, and energy-efficient monitoring of substations.

District heating (DH) networks are vital in the transition to sustainable energy systems. Maintaining their performance requires continuous monitoring to reduce heat losses, avoid user discomfort, and support efficient operation. However, automatic fault detection and diagnosis (FDD) in DH substations remains challenging due to limited labelled data, class imbalance, heterogeneous operating conditions, privacy constraints, and the lack of standardisation. This thesis aims to develop learning-based approaches for automatic FDD in DH substations that can operate under these real-world industry constraints. Therefore, this work focuses on scalable representations, transferability across domains, and robustness against scarce and imbalanced fault data, and includes eight studies that jointly develop and validate learning-based FDD methods for DH substations. Across these studies, the work advances unsupervised locality-based anomaly detection, hybrid and augmentation-enhanced fault diagnosis, transfer and cross-modal learning for label-scarce settings, privacy-preserving semi-supervised federated learning, and streaming representations for continuous fleet-scale monitoring. The results demonstrate that reliable, scalable FDD in DH systems can be achieved despite severe field constraints. By combining topology inference for local analysis, data representation, knowledge transfer and data augmentation, this thesis advances practical, deployable FDD intelligence to support more resilient, efficient, and data-smart DH networks aligned with the climate and digitalisation goals.

This paper proposes SCENTS, a Streaming Consensus Embedding for Time Series that fuses streaming windows into a single low-dimensional representation suitable for diverse downstream tasks. First, it learns a denoised low-dimensional latent basis for state initialisation (). Second, for each newly arriving stream, it performs a near-linear time, multi-pass consensus update that fuses new affinities directly into Z. We prove convergence and validate our assumptions using various real-world multi-source industrial datasets on a streaming consensus clustering task. In contrast to conventional pipelines that accumulate noise over time, require costly  co-association matrices, or  eigendecompositions, SCENTS yields a linear memory consensus embedding that improves monotonically along the streaming horizon and produces high-quality partitions. Moreover, SCENTS is designed to be integrated with any fixed encoder, enabling the implementation of a lightweight streaming consensus adaptation layer. The resulting embeddings offer a compact, reusable representation that supports various downstream tasks beyond clustering, thereby providing a scalable and generalisable fusion layer for data analysis.

Fault Detection and Diagnosis (FDD) in District Heating (DH) systems is vital for improving operational efficiency. As DH networks evolve towards Fourth-Generation District Heating (4GDH), their reliance on lower operational temperatures intensifies the need for robust FDD. However, implementing effective FDD faces challenges due to the lack of labelled fault data and the complexity of DH substations. This paper introduces a novel FDD methodology using Transfer Learning (TL) to bridge the gap between faulty bearings, controlled DH substation experiments and real-world operational data. We propose a fault signature method that aligns the data to reduce the domain gap, revealing similarities between faulty bearings’ vibration patterns and ΔT readings in DH substations. The TL-enhanced models demonstrated robust performance, achieving F1 scores up to 98% on lab data and 91% on real-world operational data, respectively. These results mark a notable advancement in FDD of DH substations, as our method offers accurate fault detection and valuable insights across diverse operational contexts, ranging from valve issues, faulty sensors, wrong control strategies and normal behaviour. Notably, temperature dynamics resemble behaviour akin to faulty bearing vibrations, highlighting their potential as a critical indicator of a faulty substation, enabling more effective FDD in DH systems.

This study introduces FLAME (Fault Localization using Augmented Model Enhancement), a novel fault diagnosis framework for District Heating (DH) substations. Automated Fault Detection and Diagnosis (FDD) has become imperative as many DH substations perform sub-optimal due to faults. The main challenges complicating accurate fault diagnosis are increasing operational complexities and a scarcity of labelled data.

FLAME integrates a hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) model with an attention mechanism and introduces the Fault Augmentation Signature Technique (FAST). FAST overcomes the limitations of traditional stochastic data augmentation methods by leveraging pattern mixing of the time series. The FLAME framework uses transfer learning, initially trained on augmented data using FAST and fine-tuned using original substation data.

Experimental results reveal that FLAME outperforms conventional methods, obtaining F1 scores of approximately 0.95 and 0.92 on lab-simulated and real-world datasets, respectively. Additionally, the research found the importance of the temperature difference measurement (ΔT) and median-based sampling strategies for optimal fault pattern identification.

These findings establish FLAME as a new benchmark in DH system diagnostics, offering a robust framework to enhance fault diagnosis accuracy and operational efficiency of DH substations. 

Fault detection in district heating (DH) substations is crucial for maintaining energy efficiency. However, existing methods often fall short and rely on labelled data or global analysis that may miss subtle anomalies. We introduce HEAT, a Hierarchical-constrained Encoder-Assisted Time series clustering method designed to enhance fault detection in DH substations. HEAT operates in a two-phase approach: first, it approximates a relative network topology using a constraint hierarchical clustering algorithm on supply temperature profiles. HEAT incorporates a Convolutional AutoEncoder (CAE) for dimensionality reduction of the time series data and uses adaptive soft constraints in the linkage function, enabling both minimum and maximum cluster size constraints while supporting domain knowledge, e.g., must-link and cannot-link constraints, using a constraint matrix. Second, we use the topology approximation to perform intra-cluster analysis using Mean Absolute Deviation (MAD) z-scores, with neighbouring substations serving as a validation mechanism, allowing for robust analysis without requiring labelled data. Experimental results demonstrate that HEAT outperforms conventional clustering methods while achieving 74.1% sensitivity and 95.5% specificity in fault detection, significantly improving over typical global analysis. HEAT not only identified major faults (e.g., sensor issues, valve failures) but also detected subtle anomalies (e.g., secondary leakages) while minimising false positives. This unsupervised method offers a viable and flexible solution for DH networks, improving operational efficiency and energy sustainability without disclosing sensitive information.

This study introduces a novel systematic approach to address the challenge of labeled data scarcity for fault detection and diagnosis (FDD) in District Heating (DH) systems. To replicate real-world DH fault scenarios, we have created a controlled laboratory emulation of a generic DH substation integrated with a climate chamber. Furthermore, we present an FDD pipeline using an isolation forest and a one-class support vector machine for fault detection alongside a random forest and a support vector machine for fault diagnosis. Our research analyzed the impact of data sampling frequencies on the FDD models, revealing that shorter intervals, such as 1-min and 5-min, significantly improve FDD performance. We provide detailed information on six scenarios, including normal operation, a minor valve leak, a valve leak, a stuck valve, a high heat curve, and a temperature sensor deviation. For each scenario, we present their signature, quantifying their unique behavior and providing deeper insights into the operational implications. The signatures suggest that, while variable, faults have a consistent pattern seen in the generic DH substation. While this work contributes directly to the DH field, our methodology also extends its applicability to a broader context where labeled data is scarce. © 2024 The Authors

District Heating (DH) systems are essential for energy-efficient urban heating. However, despite the advancements in automated fault detection and diagnosis (FDD), DH still faces challenges in operational faults that impact efficiency. This study introduces the Shared Nearest Neighbor Enhanced District Heating Anomaly Detection (SHEDAD) approach, designed to approximate the DH network topology and allow for local anomaly detection without disclosing sensitive information, such as substation locations. The approach leverages a multi-Adaptive k-Nearest Neighbor (k-NN) graph to improve the initial neighborhood creation. Moreover, it introduces a merging technique that reduces noise and eliminates trivial edges. We use the Median Absolute Deviation (MAD) and modified z-scores to flag anomalous substations. The results reveal that SHEDAD outperforms traditional clustering methods, achieving significantly lower intra-cluster variance and distance. Additionally, SHEDAD effectively isolates and identifies two distinct categories of anomalies: supply temperatures and substation performance. We identified 30 anomalous substations and reached a sensitivity of approximately 65% and specificity of approximately 97%. By focusing on this subset of poor-performing substations in the network, SHEDAD enables more targeted and effective maintenance interventions, which can reduce energy usage while optimizing network performance. © 2024 IEEE.

This paper presents a comprehensive survey of state-of-the-art intelligent fault detection and diagnosis in district heating systems. Maintaining an efficient district heating system is crucial, as faults can lead to increased heat loss, customer discomfort, and operational cost. Intelligent fault detection and diagnosis can help to identify and diagnose faulty behavior automatically by utilizing artificial intelligence or machine learning. In our survey, we review and discuss 57 papers published in the last 12 years, highlight the recent trends, identify current research gaps, discuss the limitations of current techniques, and provide recommendations for future studies in this area. While there is an increasing interest in the topic, and the past five years have shown much advancement, the absence of open-source high-quality labeled data severely hinders progress. Future research should aim to explore transfer learning, domain adaptation, and semi-supervised learning to improve current performance. Additionally, a researcher should increase knowledge of district heating data using data-centric approaches to establish a solid foundation for future fault detection and diagnosis in district heating.