1. Introduction

2. Methodology

Table 1. List of Digital Twin Applied to Fault Monitoring Papers

3. Digital Twin Architecture

4. Digital Twin in Fault Monitoring

4.1. Equipment-Level Application

One of the main applications and a driver for the Industry 4.0 revolution is the PHM systems of various industrial components/equipment [87]. From here the equipment-level application of DT in different industries will be discussed. [49] discusses a novel implementation method that combines DT with the FD of large industrial equipment, focused on rolling bearings. The proposed framework was evaluated by comparing real-world and simulated fault signals of the rolling bearing giving information on the performance of the inner and outer race, using a sampling frequency of 12 kHz and 1750 rpm test speed. Pearson, Spearman, and Kendall statistical methods were used to compare the signals yielding a p-value < 0.05 indicating that the simulation signal significantly matches the real-world signal. While the study of [22] not only developed a technique for bearings but also concentrated on the diagnosis of crack types and sizes. This was achieved this by utilizing a combination of a strict-feedback backstepping DT and a machine learning algorithm. This approach involved designing a DT that will model and estimate acoustic emission signals, generate an acoustic emission residual signal, and using a support vector machine to classify the crack type and size. To test the efficiency of the proposed technique, the bearing dataset from the Ulsan Industrial Artificial Intelligence Laboratory was used which resulted in an average accuracy of 97.13% and 96.9% for crack type diagnosis and crack size diagnosis, respectively. For aviation, there is a significant difficulty in observing the status of an aircraft engine using traditional diagnosis methods. To aid in this difficulty, [18] proposes a hybrid FD method for the turbofan engine TFE-731 by which the status can be monitored online featuring its high suitability for on-time monitoring. The proposed method employs Fast Orthogonal Search (FOS) due to its superiority in time-frequency analysis and spectral estimation than the fast Fourier transform due to FOS’s capability in finding orthogonal bases, and the Autoregressive moving average (ARMA) model as it is widely used for the system output prediction of DTs and is able to predict the RUL of a valve of an aircraft engine. Practical implementation of the FOS-based ARMA data-driven approach resulting to a lower root mean square error and higher %variance accounted for (%VAF) of the FOS model, as compared with the results of the original model, validates its real-time effectiveness showing high prediction accuracy and stability, suggesting that the method can be utilized in examining the irregular parts of an aircraft engine. In contrast, [17] focuses on the sensor diagnostics and PHM of the Wärtsilä 9L50DF marine dual fuel engine. The paper presents an Engine Diagnostics System (EDS) that enables intelligent engine monitoring, advanced sensor fault detection, and timely corrective actions. The proposed Unified Digital System (UDS) integrates the EDS with the diagnostic and control system of the engine, combining a data-driven model with functional and thermodynamic approaches to comprehensively diagnose sensor abnormalities, faults, and failures. Results of the simulation show the effectiveness of the UDS concept, showing that it can capture engine sensor irregularities and restore engine operations to its original state by applying fitting corrective actions. For automotive, centered on Li-ion battery packs of electric vehicles, [57] used a holistic data pipeline to transfer information such as the State of Charge (SOC) and State of Health (SOH) by reverse-engineering the diagnostics interface of a 2014 e-Golf to query for Unified Diagnostic Services (UDS) messages containing both battery pack and cell-individual data. An On Board Diagnosis (OBD) logger was used to record the data with edge-processing capability, which was pushed into the cloud twin system using IoT technology. Battery models were fitted to the data, and cell individual internal resistance was inferred. State estimation of the battery was presented, and the data was coherent and relatively comparable to those from the literature. However, the confidence interval (CI) only amounted to 50%, and there is also a need for improvement in the reverse engineering of the battery as some of the internal resistance results had high variance requiring large sample amounts to establish statistically precise results.

Table 2. Equipment Level DT in FM

Table 2. Equipment Level DT in FM

PY	Ref	Industry	Application	Prediction Method	Proposed Algorithm	Performance
2021	[49]	Manufacturing	Deep Groove Ball Bearing	Data-Driven	Detail Parameter	r = 0.79, p <0.05
2021	[22]	Industrial	Cylindrical Rolling Bearing	Hybrid	Strict Feedback DT and ML	Acc: 97.13%
2021	[18]	Aviation	Turbofan Engine	Hybrid	FOS-based ARMA	%VAF = 99.9%
2022	[17]	Maritime	Diesel Engine	Data-Driven	Unified Digital System	%Error = 1.1%
2022	[57]	Automotive	Battery Packs	Hybrid	OBD Data to Cloud-based DT	CI = 50%

4.2. System-Level Application

In the domain of system-level applications, [26] provides a framework for integrating multiple data sources and modeling techniques to enable accurate diagnosis and prognosis of equipment faults in a factory setting. The networked proximity sensors collect real-time data from the factory floor. At the same time, the maintenance log data is used to construct a high-fidelity simulation of each piece of equipment using a data-tagging technique. This simulation is then used to determine fault relationships and train a DT model, which is integrated into a detailed DES model of the factory. The co-simulation approach is demonstrated in a case study of a manufacturing line, showing that it can accurately predict equipment failures and optimize maintenance schedules to reduce downtime and improve productivity. This approach will be used to train Bayesian Networks (BNs) as a Machine Learning (ML) suitable algorithm to represent multilevel faults of complex hierarchical system. Furthermore, BN allows the determination of how faults spread within and between different categories of faults, and a single model can function for both prediction and diagnostics purposes without requiring any additional training, and the model can also account for the related uncertainties. The implementation of SIA algorithm is also discussed which enables the detection of all possible directed edges and the distinction between a parent and an ancestor node of the BN. To avoid damaging the equipment only non-invasive faults were examined in the testbed however the same approach can possibly be used for invasive faults. The validation experiments determined the structural hamming distance (SHD) metric, and using the time to failure as its parameter. Reduction of this parameter results into an enhanced BN structure, while increasing it does the opposite. This shows the accuracy from the proposed approach is superior than traditional methods. [20] focused on the improvement of operation efficiency of micro-grids by developing a smart framework based on cloud-edge integration used for the diagnosis of a micro-grid composed of three sub-micro-grids with different photovoltaic arrays under different operating conditions. The developed intelligent framework consists of three stages: Firstly, a micro-grid DT model was created on the CloudPSS platform using a DT approach. Secondly, a neural network-based FD model was developed by training it on the cloud server, using data from DT model. Finally, the trained model is downloaded to the edge device to conduct offline FD of the micro-grid, which reduces the computational load on the cloud server. This is particularly important for maintaining the security and reliability of data storage and transmission. The neural network uses a Rectified Linear Unit (ReLU) activation function, and as the number of iterations increased, the loss value of the model decreased, resulting in a 95% accuracy. [16] discussed a model-based system-level DT FD of thermal-hydraulic high-pressure feedwater systems present in nuclear power plants. The model-based approach makes it possible for virtual sensors to be utilized in the multi-component system, and be placed in areas that would not be otherwise possible due to physical domain sensor restraints which results in improved FD capabilities and automated monitoring of the DT model. The model construction begins with the decomposition of the system into its operational building blocks with functionalities that can be explained by first principles. Measurements taken by the physical sensors are used to create the model, in hand with the virtual sensors that are used to determine the unmeasured process variables, relying on a combination of conservation laws and relations of components present in the system. After the measurements have been identified from both sensors, these will be used for the model calibration. The result of their model validation showed that their approach was able to indicate a recurring abnormality during the startup of the tested feedwater component, as confirmed by the plant operator. The work of [42] provides a simple discussion on the intelligent monitoring and maintenance of power grid substations using DT. The current approach for monitoring of substations often presents complexities due to the independence of equipment and system stages, from their design to operation and maintenance. This results in difficulty with creating a holistic full life cycle of the substation. DT application will provide improved operation monitoring by reconstructing both the substation and its environment, then the reconstructed model is fused with the mapping of the physical substation and thus enable a two-way coordinated interaction, real-time monitoring, FD, and security event warning. A DT estimator is initially used in the DT approach of [65] for the FD of distributed photovoltaic (PV) systems. The DT estimator can be either of the three DT Prediction Methods and analytically measures the real-time characteristics of a system and creates a digital emulation. Unique advantages (high energy yield, scalability, performance reliability) of PV systems when applied in architecture are highly attractive. However, these systems are prone to myriad faults that degrade performance which is attributed to their complex outdoor installations, aging, and equipment weathering. Moreover, the faults occurring at PV energy conversion units (PVECUs) are difficult to discover and repair as the PVECUs are numerous in each system. Hence, a robust DT approach for FD and Fault Identification (FI) that makes use of the measured characteristics of the PVECU and compares with the physical component to generate an error residual vector. The generated vector is analytically evaluated to diagnose the presence of a fault and its PVECU component (PV Panel, Power Converter, Electrical Sensor) origin. Experimental results demonstrate that the approach performs well in realistic outdoor conditions, and is capable of recognizing real-time faults and precisely classifying their origin with relatively higher sensitivity and robustness (yielding a FI window = 2ms) as compared with existing approaches.

Table 3. System Level DT in FM

Table 3. System Level DT in FM

PY	Ref	Industry	Application	Prediction Method	Proposed Algorithm	Performance
2021	[26]	Manufacturing	Assembly Line Robots	Data-Driven	Structural Intervention	SHD Score = 9
2021	[20]	Energy	Microgrid	Data-Driven	Connected Neural Networks	Acc: 95%
2021	[16]	Nuclear	High-Pressure Feedwater System	Model-Based	Mass Balanced Virtual Sensors	N/A
2022	[42]	Energy	Power-Grid Equipment	Hybrid	N/A	N/A
2022	[65]	Energy	Smart Building	Model-Based	Prototype Validation	Small FI Window = 2ms

While the implementation of DT in FM presents unique benefits, there are also several challenges that need to be addressed to refine the application of this technology. One main challenge is the lack of standardization and interoperability among different equipment and systems leading to difficulties when integrating existing monitoring systems with proposed DT models [20]. This results in a lack of scalability, limited data sharing, and increased costs due to customized solutions across different systems. As DT technology relies heavily on data used for DT model calibration, the accuracy and reliability of data are vital, which can be affected by factors such as consistency and completeness of the data, and the quality of the sensors. This requires precise selection, calibration, sensor placement, and data pre-processing techniques. An interesting aid to this problem would be the approach presented in [16], using virtual sensors to secure the completeness of data. Tremendous amounts of data are required for the model creation which can result in privacy and security concerns. It is critical to guarantee the confidentiality, integrity, and quality of the data for the successful implementation of DT in FM [31]. Significant computational resources are essential which can be an issue for systems with constrained resources and it is, therefore, necessary to continuously develop efficient and scalable algorithms. The development of accurate and reliable DT models requires a deep understanding of the underlying processes of equipment or a system wherein, the lack of domain knowledge and expertise presents itself as a significant challenge.

There are also several opportunities that can be leveraged by the implementation of DT in FM. One of which is the ability to improve from the current reactive maintenance approach to a more proactive predictive maintenance approach. Through real-time monitoring and prediction of impending failures of the equipment/system, maintenance activities can be performed before failure occurs which can promote increased equipment/system uptime and significant cost savings. In addition, DT in FM allows the development of more precise and reliable prognostic models that would greatly aid equipment design optimization leading to increased efficiency and improved product quality. Furthermore, this can also enable the data integration of different equipment and different systems to obtain a more comprehensive view of the entire system, establishing improved system-level monitoring and optimization. The development of new business models providing real-time information on the equipment/system and conducting predictive maintenance can be explored to increase revenue streams and improve customer satisfaction.

5. Conclusions

Abbreviations

References

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