Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

Gálvez, Antonio; Diez-Olivan, Alberto; Seneviratne, Dammika; Galar, Diego

doi:10.3390/su13126828

Cited by 19 publications

(11 citation statements)

References 64 publications

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“…Algorithms that are in the minority but are nevertheless used in the context of supervised learning for HVAC FDD systems are: extreme gradient boosting (XGBoost) [ 46 , 93 ], supervised auto-encoder (SAE) [ 53 ], diagnostic multi-query graph (DMG) [ 75 ], hidden Markov models (HMM) [ 74 ], and ensemble models such the ones based on k-nearest neighbor (KNN) [ 64 ] or based on boosted regression trees (BRT) [ 80 ].…”

Section: Results Part I: Review and New Classification Of Fdd Approac...mentioning

confidence: 99%

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

Matetić

Štajduhar

Wolf

et al. 2022

Sensors

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Heating, ventilation, and air conditioning (HVAC) systems are a popular research topic because buildings’ energy is mostly used for heating and/or cooling. These systems heavily rely on sensory measurements and typically make an integral part of the smart building concept. As such, they require the implementation of fault detection and diagnosis (FDD) methodologies, which should assist users in maintaining comfort while consuming minimal energy. Despite the fact that FDD approaches are a well-researched subject, not just for improving the operation of HVAC systems but also for a wider range of systems in industrial processes, there is a lack of application in commercial buildings due to their complexity and low transferability. The aim of this review paper is to present and systematize cutting-edge FDD methodologies, encompassing approaches and special techniques that can be applied in HVAC systems, as well as to provide best-practice heuristics for researchers and solution developers in this domain. While the literature analysis targets the FDD perspective, the main focus is put on the data-driven approach, which covers commonly used models and data pre-processing techniques in the field. Data-driven techniques and FDD solutions based on them, which are most commonly used in recent HVAC research, form the backbone of our study, while alternative FDD approaches are also presented and classified to properly contextualize and round out the review.

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Section: Results Part I: Review and New Classification Of Fdd Approac...mentioning

confidence: 99%

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

Matetić

Štajduhar

Wolf

et al. 2022

Sensors

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“…Railroad grouping stations are generally equipped with relatively complete shunting yards and shunting equipment and are the main body of the railroad scheduling operation plan. It is mainly responsible for the arrival, disassembly, grouping, and departure of cargo trains, through train operation and other train operations [7]. us, the main work of a grouping station is to provide the train service, as shown in Figure 2.…”

Section: State Of the Artmentioning

confidence: 99%

“…To describe the moment of locking and unlocking of the approach, 0-1 variables p and g are established. Constraints (7) and (8) ensure that the same set of 0-1 variables for the shunting plant can only change once over time and can only change from 0 to 1, thus ensuring that the state transition from "not yet occurred" to "already occurred" for the approach locking or unlocking event can only occur at most once. e state change from "not yet occurred" to "already occurred" can only occur at most once.…”

Section: Intelligent Body Behavior Constraintsmentioning

confidence: 99%

Quality Analysis of Railroad Train Shunting Operation Plan Using the Intelligent Body Model

Tao

2022

Advances in Multimedia

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Shunting work is an important component part of railway transport production and is an important link in realizing the train marshalling plan and the train operation map, accelerating the rotation of vehicles, and completing the task of transport production. With the present, the gradual construction of passenger dedicated lines and high-speed train operation bring a new dawn to railroad transportation on the one hand, but on the other hand, high-speed train operation makes the process of train operation more complicated, and the safety hazards and uncontrollable factors of trains are higher than before. Compared with existing lines, passenger dedicated lines put forward higher requirements on the performance of train operation and dispatching system, requiring better real-time, intelligence, and interactive response characteristics of the train operation and dispatching system. The construction of passenger dedicated lines and the increase in train operation speed have put forward higher requirements for safe, on-time, and high-efficiency train operation. The train operation scheduling system is one of the cores of intelligent railroad transportation system, which is the guarantee of safe and on-time train operation. This requires the search for new control strategies to meet the requirements of high-speed train operation and multiple objectives of safety, punctuality, and comfort. Train operation scheduling is a typical distributed system. At present, there are still problems in the safety of railway shunting operations, such as disconnection in management and implementation, failure to revise some rules and regulations, frequent occurrence of obstacles in equipment and facilities, and substandard quality of operators. Therefore, it is necessary to make a systematic analysis of the current situation of railway shunting safety and propose countermeasures and measures. By studying the structure of intelligent body and multi-intelligent body theory, it decomposes the influencing factors of railroad shunting operation, designs the intelligent body model of each element, and applies it in train shunting operation to improve the quality of railroad shunting operation.

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“…It was also previously used to generate synthetic data to build a datadriven model for multiple fault detection; the model was trained, validated, and tested using real data and synthetic data. The methodology proposed to combine the two types of models and fuse the data sources obtained a remarkable accuracy [50].…”

Section: A Physics-based Model Of the Hvac Systemmentioning

confidence: 99%

“…Since the physics-based model used in this research and its capability to generate data in a faulty state were already developed and validated [49], [50], we validate the parameters monitored with virtual sensors, including pressure after air filter, pressure before air filter, and mass flow rate, using the previously generated data.…”

Section: A Physics-based Model Of the Hvac Systemmentioning

confidence: 99%

A Hybrid Model-Based Approach on Prognostics for Railway HVAC

2022

Self Cite

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Prognostics and health management (PHM) of systems usually depends on appropriate prior knowledge and sufficient condition monitoring (CM) data on critical components' degradation process to appropriately estimate the remaining useful life (RUL). A failure of complex or critical systems such as heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage may adversely affect people or the environment. Critical systems must meet restrictive regulations and standards, and this usually results in an early replacement of components. Therefore, the CM datasets lack data on advanced stages of degradation, and this has a significant impact on developing robust diagnostics and prognostics processes. This paper proposes a methodology for implementing a hybrid model-based approach (HyMA) to overcome the limited representativeness of the training dataset for developing a prognostic model. The proposed methodology is evaluated building an HyMA which fuses information from a physics-based model with a deep learning algorithm to implement a prognostics process for a complex and critical system. The physics-based model of the HVAC system is used to generate runto-failure data. This model is built and validated using information and data on the real asset; the failures are modelled according to expert knowledge and an experimental test to evaluate the behaviour of the HVAC system while working, with the air filter at different levels of degradation. In addition to using the sensors located in the real system, we model virtual sensors to observe parameters related to system components' health. The run-to-failure datasets generated are normalized and directly used as inputs to a deep convolutional neural network (CNN) for RUL estimation. The effectiveness of the proposed methodology and approach is evaluated on datasets containing the air filter's run-to-failure data. The experimental results show remarkable accuracy in the RUL estimation, thereby suggesting the proposed HyMA and methodology offer a promising approach for PHM.INDEX TERMS Prognostics and health management, Hybrid Modelling, Deep learning, HVAC system, railway.

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Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

Cited by 19 publications

References 64 publications

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

Quality Analysis of Railroad Train Shunting Operation Plan Using the Intelligent Body Model

A Hybrid Model-Based Approach on Prognostics for Railway HVAC

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