# Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities

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## Abstract

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## 1. Introduction

- We provide a comprehensive overview of state-of-the-art intelligent FDD in DH.
- We provide an elaborated overview in research papers based on fault detection, fault diagnosis or data mining, as well as current trends, as depicted in Figure 1.
- We provide an in-depth Strengths,Weaknesses, Opportunities, and Threats (SWOT) analysis to identify industry challenges, research gaps, and opportunities.
- We provide a clear list of research directions in the form of recommendations, and explain several advantages and disadvantages.

## 2. Background

#### 2.1. District Heating

#### 2.2. Automatic Fault Handling

#### 2.3. Machine Learning

- (i)
- Supervised learning [11] (predictive) is concerned with learning mappings between inputs $\mathbf{x}$ and outputs y given a labeled data set of input-output pairs, i.e., the output is a label that represents the class type of the inputs $\mathbf{x}$. Supervised learning can be divided into two types: classification and regression. Classification refers to classifying with discrete values as output, e.g., industrial or residential, i.e., classification attempts to predict class membership (assign a label). If the labels are numerical in a continuous range, it is called regression, i.e., regression attempts to predict numerical values, e.g., energy demand in the next 24 h. The algorithms fit a model to the labeled data set and can classify or predict new unseen data based on the independent variables as input. Some techniques in supervised learning include Linear Regression (LR), Support Vector Machines (SVM), Naive Bayes (NB), or Random Forests (RF).
- (ii)
- Unsupervised learning [10] (descriptive) is the technique of discovering underlying structures in data. Unsupervised learning can help identify essential (statistical) characteristics and patterns within the data without human intervention. It is a crucial paradigm in DMKD. Unsupervised learning is ideal for explanatory data analysis, outlier detection, and image or pattern recognition. Consequently, unsupervised learning can also be used for data pre-processing, e.g., dimensional reduction techniques such as Principle Component Analysis (PCA). Some techniques in unsupervised learning include k-Means (kM), Gaussian Mixture Model (GMM), or Linear Discriminant Analysis (LDA).
- (iii)
- Reinforcement learning [12] is the technique where an agent learns in a particular environment through exploration and exploitation. The agent performs specific actions that lead to a reward or punishment, aiming to maximize the reward. An agent should perform actions known to produce a high reward; however, the agent has to learn such actions by trial and error. That is, the algorithm rewards the agent for reinforcing the preferred behavior. The model continues to learn until it converges or achieves its stopping criteria. A well-known technique in reinforcement learning is Q-learning, which has a broad set of application areas such as self-driving or gaming AI.
- (iv)
- Deep learning [13] applies to any of the paradigms mentioned above in case one or more of the employed regressors or classifiers is a Deep Neural Network (DNN). Deep refers to using a neural network consisting of three or more layers. DNN can handle unstructured data sets, such as texts or images. Recently, deep learning has made a significant impact in text and image generation[14,15]. Also, DNN can automate feature extraction, such as Convolutional Neural Network (CNN), which reduces the need for human interventions; a side effect is that reasoning about model behavior becomes significantly more complicated, as the models are incredibly complex (black box models). However, an upcoming field, called explainable AI, tries to mitigate this problem. Explainable AI refers to the ability of complex models, such as in deep learning, to explain their reasoning or decision-making process such that it can be understood by humans, i.e., it can provide a transparent and understandable explanation for how a model arrived at its output or recommendation. A brief overview is given in [16]. Some techniques in deep learning include Multilayer Perceptron (MLP), CNN, or Long short-Term Memory (LSTM).
- (v)
- Semi-supervised learning [17], as the name indicates, provides hybrid solutions combining supervised and unsupervised learning techniques. It can use smaller labeled data sets to classify or extract patterns from larger unlabeled data sets. Compared to a traditional classifier, semi-supervised learning can reduce the size of the original labeled data set by 66%, at the cost of five times as many unlabeled data [18]. Semi-supervised learning is beneficial in scenarios where unlabeled data is abundant, but labeled data is expensive—typical in most engineering scenarios. Compared to the preceding paradigms, semi-supervised learning is less explored. Some techniques include MixMatch [19], label propagation [20], or self-training [21].
- (vi)
- Transfer learning [22] is a technique where a model trained for one task (source domain) is reused, e.g., as a starting point, in a second but related task (target domain). Unlike semi-supervised learning, where the model exploits the abundance of unlabeled data, transfer learning exploits the models available in similar domains. A subcategory of transfer learning is domain adaptation [23], which mainly focuses on using labeled data in one or more similar domains—assuming the domains shares class labels. It is similar to supervised learning, where the goal is to find a mapping based on training data, and the model predicts test data assumed to be from the same data distribution as the training data. In domain adaption, the training data comes from a particular domain with a large set of labeled data. The model can predict in another similar domain under the same assumptions as supervised learning—test data is from the same distribution as the training data. Transfer learning and domain adaption can be helpful in DH as it reduces the need for labeled data, which is currently scarce in DH.

## 3. Related Work

## 4. Method

## 5. District Heating Data Collection

## 6. Current Intelligent Techniques for FDD

#### 6.1. Fault Detection

#### 6.1.1. Data Mining and Knowledge Discovery

#### 6.1.2. Outlier Detection

#### 6.1.3. Leakage Detection

#### 6.2. Fault Diagnosis

#### 6.2.1. Sensor Failure

#### 6.2.2. Fouling

#### 6.2.3. Valves

#### 6.2.4. Pipes

#### 6.2.5. Multi-Label Classification

## 7. Discussion

#### 7.1. Strengths

#### 7.2. Threats

**Recommendation 1:**Policymakers and industry should make serious attempts to create legislation and facilitate standardization of (secondary) data collection and installation of (monitoring) equipment.

**Recommendation 2:**Researchers should investigate the effects of secondary data or additional sensor placement on model prediction performance, to generate knowledge for standardization in DH (data collection).

#### 7.3. Weaknesses

**Recommendation 3:**Improve and increase knowledge on characteristics and properties of DH data using data-centric approaches, which guides future work in intelligent FDD, e.g., association analysis studies, dimensional reduction studies, and DMKD studies.

**Recommendation 4:**Increasing known ground truth information (optimal, sub-optimal, faulty), which can be used to train more accurate models, but more importantly, evaluate the performance.

#### 7.4. Opportunities

**Recommendation 5:**Experiment and increase knowledge using ML methods for the generation of realistic synthetic data, such as generative machine learning algorithms, e.g., using VAE or GAN.

**Recommendation 6:**Implementation of hybrid models that combine real-world and simulated data for FDD in DH. The approach can provide more insights and identify patterns and relationships that training on one type of data may miss.

**Recommendation 7:**Explore the use of transfer learning and domain adaptation for FDD in DH. The knowledge from other domains, e.g., building energy management or industrial systems, can help improve the performance of FDD models in DH. Consequently, both techniques are useful for reducing the need for labeled data.

**Recommendation 8:**Investigate and utilize semi-supervised learning for FDD in DH by combining both labeled and unlabeled data for training FDD models. The technique can help improve performance by exploiting a large number of unlabeled data.

## 8. Conclusions

- Transfer learning.
- Domain adaption.
- Semi-supervised learning.
- Hybrid models.

- Data-centric approaches.
- Improving (labeled) data quality.
- Quantifying district heating definitions.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

DH | District Heating |

CHP | Combined Heat and Power |

DHW | Domestic Hot Water system |

FDD | Fault Detection and Diagnosis |

4GDH | 4th generation district heating |

5GDH | 5th generation district heating |

DMKD | Data Mining and Knowledge Discovery |

AI | Artificial Intelligence |

ML | Machine Learning |

HOM | Higher Order Mining |

EDA | Exploratory Data Analysis |

GMM | Gaussian Mixture Model |

KGMM | Kernel Gaussian Mixture Model |

SVM | Support Vector Machines |

kNN | k-Nearest Neighbor |

kM | k-Means |

kS | k-Shape |

PAM | Partitioning Around Medoids |

MIA | Mean Index Adequacy |

CDI | Cluster Dispersion Indicator |

SI | Silhouette Index |

DBI | Davies-Bouldin Index |

BIC | Bayesian Information Criterion |

PCC | Pearson Correlation Coefficient |

CC | Conformal Clustering |

MST | Minimum Spanning Tree |

TPOT | Tree-based Pipeline Optimization Tool |

AHC | Agglomerative Hierarchical Clustering |

LSTM | Long short-Term Memory |

ANN | Artificial Neural Network |

CNN | Convolutional Neural Network |

DNN | Deep Neural Network |

GAN | Generative Adviserial Networks |

MLP | Multilayer Perceptron |

GBDT | Gradient Boosted Decision Tree |

BDT | Binary Decision Tree |

DT | Decision Tree |

DTR | Decision Tree Regression |

CB | Contextual Bandit |

NB | Naive Bayes |

RUSBT | RUS Boosted Trees |

XGBoost | Extreme Gradient Boosting |

RF | Random Forests |

IF | Isolation Forests |

AB | Adaptive Boosting |

ERT | Extremely Random Tree |

LDA | Linear Discriminant Analysis |

PCA | Principle Component Analysis |

BN | Bayesian Network |

ARIMA | Auto Regressive Integrated Moving Average |

LinUCB | Linear Upper Confidence Bound |

CART | Classification and Regression Tree |

AE | Auto Encoder |

VAE | Variational Auto Encoder |

LR | Linear Regression |

RR | Robust Regression |

LOR | Logistic Regression |

RFR | Random Forest Regression |

AR | auto Regression |

LASSO | Lasso Regression |

RIDGE | Ridge Regression |

GBR | Gradient Boosting Regression |

PLS | Partial Least Squares Regression |

SVR | Support Vector Regression |

OLS | Ordinary Least Squares |

COC | Continuous Operation Control |

NSB | Night Setback Control |

TCO5 | Time Clock Operation (during five workdays) |

TCO7 | Time Clock Operation (during seven workdays) |

HVAC | heating, ventilation, and air conditioning |

OCR | Optical Character Recognition |

SC | Saliency computation |

SHAP | Shapely Additive Explanations |

EM | Expectation Maximization |

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Inclusion | Exclusion |
---|---|

Available in electronic form | Duplicates |

Peer-reviewed journal and conference papers | Non-relevant title or abstract |

Written in English | Non-indexed studies |

Addresses FDD in DH | Research thesis |

Published between 2010 and 2022 |

Feature | Notation | Unit |
---|---|---|

Primary supply temperature | ${T}_{ps}$ | °C |

Primary return temperature | ${T}_{pr}$ | °C |

Volume flow | $\dot{V}$ | m${}^{3}/$h |

Accumulated volume | V | m${}^{3}$ |

Accumulated energy | Q | J |

References | Methodologies | Distance Metrics | Validation Metrics |
---|---|---|---|

Tureczek et al. [34] | kM | Euclidean distance | MIA, CDI, DBI and SI |

Gianniou et al. [41] | kM | K-Spectral Centroid | BIC, SI |

Hong et al. [46] | kM | Euclidean distance | DBI |

Flath et al. [44] | kM | Euclidean distance | DBI |

Xue et al. [48] | kM, PAM | Euclidean distance | DBI |

Ma et al. [42] | PAM | Pearson Correlation Coefficient-based dissimilarity | Dunn Index |

Calikus et al. [32] | kS | Dynamic Time Warping | SI |

Kiluk [58] | kNN | Chebyshev distance | |

Lu et al. [43] | GMM | Probability distribution | BIC, Mean Absolute Percentage Error and PCC |

Lu et al. [47] | GMM | Probability distribution | BIC |

Sun et al. [33] | kM, GMM, KGMM | Euclidean distance, Probability distribution | Minimum Sum of Squared error |

Abghari et al. [49,50,51] | Affinity, Consensus | Levenshtein distance | SI, Adjusted Rand score |

References | Methodologies | Categories |
---|---|---|

Wang et al. [60] | LASSO | Regression |

Wang et al. [60] | SVR | Regression |

Månsson et al. [5], Theusch et al. [70], Calikus et al. [61], Wang et al. [66], Sandin et al. [72], Johansson and Wernstedt [73] | LR | Regression |

Calikus et al. [61] | RR | Regression |

Al Koussa and Månsson [71] | TPOT | Geometric |

Wang et al. [66], Palasz and Przysowa [68] | SVM | Geometric |

Theusch et al. [70], Farouq et al. [62,63,64] | kNN | Geometric |

Lee et al. [69], Theusch et al. [70], Sandin et al. [72] | kM | Geometric |

Farouq et al. [63] | CC | Geometric |

Sandin et al. [72] | Limit-checking | Geometric |

Palasz and Przysowa [68] | GBDT | Logical |

Brès et al. [36] | BDT | Logical |

Brès et al. [36] | CART | Logical |

Farouq et al. [64] | IF | Logical |

Wang et al. [60], Palasz and Przysowa [68] | MLP | Deep learning |

Wang et al. [60], Zhang and Fleyeh [67] | LSTM | Deep learning |

Zhang and Fleyeh [67] | AE | Deep learning |

Zhang and Fleyeh [67] | VAE | Deep learning |

Johansson and Wernstedt [73] | Visualisation | Statistical |

Gadd and Werner [6] | Manual analysis | Statistical |

References | Methodologies | Categories | Data | DH Segment |
---|---|---|---|---|

Chen et al. [74] | CB, RIDGE | Reinforcement learning | Leakage simulations | Primary |

Pierl et al. [76] | SVM, NB, RUSBT, AB | Traditional learning | Leakage simulations | Primary |

Xue et al. [77] | XGBoost | Traditional learning | Leakage simulations | Primary |

Guan et al. [75] | LR, OCR, Canny | Computer vision | Infrared thermal imagery | Secondary |

Xu et al. [78] | SC | Computer vision | Airborne thermal imagery | Primary |

Berg et al. [81] | LDA, SVM, AB, RF | Computer vision | Airborne thermal imagery | Primary |

Berg et al. [83] | LDA, SVM, AB, RF | Computer vision | Airborne thermal imagery | Primary |

Friman et al. [82] | AB | Computer vision | Airborne thermal imagery | Primary |

Hossain et al. [80] | CNN, LOR, LDA, SVM, NB, kNN, DT, RF, AB | Computer vision, Deep learning | Airborne thermal imagery | Primary |

References | Methodologies | Diagnosis |
---|---|---|

Zimmerman et al. [84] | BN | Sensor failure |

Aláiz-Moretón et al. [85] | RF, XGBoost, ERT, AB, kNN, ANN | Sensor failure |

Månsson et al. [86] | GBR, TPOT | Sensor failure |

Guelpa et al. [87] | Analytical | Fouling |

Cadei et al. [88] | ARIMA, RIDGE, one-class SVM | Fouling |

Kim et al. [89] | kM, MLP | Fouling |

Park et al. [90] | RF | Valves |

Langroudi et al. [91] | LR, DTR, RIDGE, kNN, PLS, SVM, RF, LASSO, XGBoost, ANN | Pipes |

Bahlawan et al. [92] | Analytical | Pipes |

Manservigi et al. [93] | Analytical | Pipes |

Bode et al. [94] | LR, kNN, CART, RF, NB, SVM, ANN | Multi-label |

Choi et al. [96] | AE, MLP | Multi-label |

Li et al. [95] | kNN, RF, ANN, CNN | Multi-label |

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## Share and Cite

**MDPI and ACS Style**

van Dreven, J.; Boeva, V.; Abghari, S.; Grahn, H.; Al Koussa, J.; Motoasca, E. Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities. *Electronics* **2023**, *12*, 1448.
https://doi.org/10.3390/electronics12061448

**AMA Style**

van Dreven J, Boeva V, Abghari S, Grahn H, Al Koussa J, Motoasca E. Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities. *Electronics*. 2023; 12(6):1448.
https://doi.org/10.3390/electronics12061448

**Chicago/Turabian Style**

van Dreven, Jonne, Veselka Boeva, Shahrooz Abghari, Håkan Grahn, Jad Al Koussa, and Emilia Motoasca. 2023. "Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities" *Electronics* 12, no. 6: 1448.
https://doi.org/10.3390/electronics12061448