Browsing by Author "Liu, Haochen"
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Item Open Access A cyclic self-enhancement technique for complex defect profile reconstruction based on thermographic evaluation(Springer, 2025-05) Liu, Haochen; Wang, Shuozhi; Zhao, Yifan; Deng, Kailun; Chen, ZhenmaoAlthough machine Learning has demonstrated exceptional applicability in thermographic inspection, precise defect reconstruction is still challenging, especially for complex defect profiles with limited defect sample diversity. Thus, this paper proposes a self-enhancement defect reconstruction technique based on cycle-consistent generative adversarial network (Cycle-GAN) that accurately characterises complex defect profiles and generates reliable artificial thermal images for dataset augmentation, enhancing defect characterisation. By using a synthetic dataset from simulation and experiments, the network overcomes the limited samples problem by learning the diversity of complex defects from finite element modelling and obtaining the thermography uncertainty patterns from practical experiments. Then, an iterative strategy with a self-enhancement capability optimises the characterisation accuracy and data generation performance. The designed loss function structure with cycle consistency and identity loss constrains the GAN’s transfer variation to guarantee augmented data quality and defect reconstruction accuracy simultaneously, while the self-enhancement results significantly improve accuracy in thermal images and defect profile reconstruction. The experimental results demonstrate the feasibility of the proposed method by attaining high accuracy with optimal loss norm for defect profile reconstruction with a Recall score over 0.92. The scalability investigation of different materials and defect types is also discussed, highlighting its capability for diverse thermography quantification and automated inspection scenarios.Item Open Access Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials(Elsevier, 2024-03-14) Deng, Kailun; Liu, Haochen; Cao, Jun; Yang, Lichao; Du, Weixiang; Xu, Yigeng; Zhao, Yifan; This work was partially supported by the Royal Academy of Engineering Industrial Fellowship [#grant IF2223B-110], and partially supported by the Science and Technology Department of Gansu Province Science and Technology Project Funding, 22YF7GA072.Most existing machine learning approaches for analysing thermograms mainly focus on either thermal images or pixel-wise temporal profiles of specimens. To fully leverage useful information in thermograms, this article presents a novel spatiotemporal-based deep learning model incorporating an attention mechanism. Using captured thermal image sequences, the model aims to better characterise barely visible impact damages (BVID) in composite materials caused by different impact energy levels. This model establishes the relationship between patterns of BVID in thermography and their corresponding impact energy levels by learning from spatial and temporal information simultaneously. Validation of the model using 100 composite specimens subjected to five different low-velocity impact forces demonstrates its superior performance with a classification accuracy of over 95%. The proposed approach can contribute to Structural Health Monitoring (SHM) community by enabling cause analysis of impact incidents based on predicting the potential impact energy levels. This enables more targeted predictive maintenance, which is especially significant in the aviation industry, where any impact incidents can have catastrophic consequences.Item Open Access Automatic reconstruction of irregular shape defects in pulsed thermography using deep learning neural network(Springer, 2022-07-25) Liu, Haochen; Li, Wenhan; Yang, Lichao; Deng, Kailun; Zhao, YifanQuantitative defect and damage reconstruction play a critical role in industrial quality management. Accurate defect characterisation in Infrared Thermography (IRT), as one of the widely used Non-Destructive Testing (NDT) techniques, always demands adequate pre-knowledge which poses a challenge to automatic decision-making in maintenance. This paper presents an automatic and accurate defect profile reconstruction method, taking advantage of deep learning Neural Networks (NN). Initially, a fast Finite Element Modelling (FEM) simulation of IRT is introduced for defective specimen simulation. Mask Region-based Convolution NN (Mask-RCNN) is proposed to detect and segment the defect using a single thermal frame. A dataset with a single-type-shape defect is tested to validate the feasibility. Then, a dataset with three mixed shapes of defect is inspected to evaluate the method’s capability on the defect profile reconstruction, where an accuracy over 90% on Intersection over Union (IoU) is achieved. The results are compared with several state-of-the-art of post-processing methods in IRT to demonstrate the superiority at detailed defect corners and edges. This research lays solid evidence that AI deep learning algorithms can be utilised to provide accurate defect profile reconstruction in thermography NDT, which will contribute to the research community in material degradation analysis and structural health monitoring.Item Open Access An autonomous rail-road amphibious robotic system for railway maintenance using sensor fusion and mobile manipulator(Elsevier, 2023-08-02) Liu, Haochen; Rahman, Miftahur; Rahimi, Masoumeh; Starr, Andrew; Durazo-Cardenas, Isidro; Ruiz-Carcel, Cristobal; Ompusunggu, Agusmian; Hall, Amanda; Anderson, RobertThe current maintenance of railway infrastructure replies heavily on human involvement, requiring possession of the track section during maintenance, resulting in high costs and inefficient execution. This paper proposes an autonomous rail-road amphibious robotic system for railway inspection and maintenance tasks. By virtue of its road and rail-autonomous mobility, it is able to execute the complete maintenance execution flow in multiple phases. The system provides flexible track job location access, low-cost maintenance execution, and reduced track network possession. The payload mobile manipulator and sensor fusion enhance the system's capabilities for multiple types of inspection and repair. The design of a command and control system was guided by a rule-based expert system strategy to enable remote operation of the whole system. The developed demonstrator of a track wheel accompanied unmanned ground vehicle was integrated and demonstrated in both operational and realistic track environments with multiple testing activities of remote operation, navigation, accurate job detection, inspection, and repair, confirming effective job completion and logical human interaction. The proposed method produces an outstanding hardware-software integrated robotic inspection and repair system with a high level of technological readiness for autonomous railway maintenance and intelligent railway asset management.Item Open Access Classification of barely visible impact damage in composite laminates using deep learning and pulsed thermographic inspection(Springer, 2023-01-31) Deng, Kailun; Liu, Haochen; Yang, Lichao; Addepalli, Sri; Zhao, YifanWith the increasingly comprehensive utilisation of Carbon Fibre-Reinforced Polymers (CFRP) in modern industry, defects detection and characterisation of these materials have become very important and draw significant research attention. During the past 10 years, Artificial Intelligence (AI) technologies have been attractive in this area due to their outstanding ability in complex data analysis tasks. Most current AI-based studies on damage characterisation in this field focus on damage segmentation and depth measurement, which also faces the bottleneck of lacking adequate experimental data for model training. This paper proposes a new framework to understand the relationship between Barely Visible Impact Damage features occurring in typical CFRP laminates to their corresponding controlled drop-test impact energy using a Deep Learning approach. A parametric study consisting of one hundred CFRP laminates with known material specification and identical geometric dimensions were subjected to drop-impact tests using five different impact energy levels. Then Pulsed Thermography was adopted to reveal the subsurface impact damage in these specimens and recorded damage patterns in temporal sequences of thermal images. A convolutional neural network was then employed to train models that aim to classify captured thermal photos into different groups according to their corresponding impact energy levels. Testing results of models trained from different time windows and lengths were evaluated, and the best classification accuracy of 99.75% was achieved. Finally, to increase the transparency of the proposed solution, a salience map is introduced to understand the learning source of the produced models.Item Open Access A clustering approach to detect faults with multi-component degradations in aircraft fuel systems(Elsevier, 2020-12-18) Zaporowska, Anna; Liu, Haochen; Skaf, Zakwan; Zhao, YifanAccurate fault diagnosis and prognosis can significantly increase the safety and reliability of engineering systems and also reduce the maintenance costs. There is very limited relative research reported on the fault diagnosis of a complex system with multi-component degradation. The Complex Systems (CS) problem, which features multiple components simultaneously and nonlinearly interacting with each other and corresponding environment on multiple levels, has become an essential challenge in system engineering. In CS, even a single component degradation could cause misidentification of the fault severity level and lead to serious consequences. This paper introduces a new test rig to simulate multi-component degradations of the aircraft fuel system. A data analysis approach based on machine learning classification of both the time and frequency domain features is then proposed to detect and identify the fault severity level of CS with multi-component degradation. Results show that a) the fault can be sensitively detected with an accuracy > 99%; b) the severity of fault can be identified with an accuracy of 100%.Item Open Access Data for the paper "A Dissection and Enhancement Technique for Combined Damage Characterisation in Composite Laminates using Laser-line Scanning Thermography"(Cranfield University, 2021-05-27 09:52) Liu, Haochen; Du, Robin; Yazdani, Hamed; Starr, Andrew; Zhao, YifanThis is the dataset for paper "A Dissection and Enhancement Technique for Combined Damage Characterisation in Composite Laminates using Laser-line Scanning Thermography". It contains the the simulation and experimental data for figures, tables and results in paper. The files are marked with related names.Item Open Access dataset for 'A Cyclic Self-enhancement Technique for Complex Defect Profile Reconstruction based on Thermographic Evaluation'(Cranfield University, 2023-04-05 13:01) Liu, Haochen; Wang, Shuozhi; Deng, Kailun; Zhao, YifanThe dataset for this research is the synthetic dataset combined FEM simulation and experimental results of a Pulsed Thermography (PT) inspection for flat-bottom hole defects in metal materials.Item Open Access Detectability evaluation of attributes anomaly for electronic components using pulsed thermography(Elsevier, 2020-09-16) Liu, Haochen; Tinsley, Lawrence; Addepalli, Sri; Liu, Xiaochen; Starr, Andrew; Zhao, YifanCounterfeit Electronic Components (CECs) pose a serious threat to all intellectual properties and bring fatal failure to the key industrial systems. This paper initiates the exploration of the prospect of CEC detection using pulsed thermography (PT) by proposing a detectability evaluation method for material and structural anomalies in CECs. Firstly, a numerical Finite Element Modelling (FEM) simulation approach of CEC detection using PT was established to predict the thermal response of electronic components under the heat excitation. Then, by experimental validation, FEM simulates multiple models with attribute deviations in mould compound conductivity, mould compound volumetric heat capacity and die size respectively considering experimental noise. Secondly, based on principal components analysis (PCA), the gradients of the 1st and 2nd principal components are extracted and identified as two promising classification features of distinguishing the deviation models. Thirdly, a supervised machine learning-based method was applied to classify the features to identify the range of detectability. By defining the 90% of classification accuracy as the detectable threshold, the detectability ranges of deviation in three attributes have been quantitively evaluated respectively. The promising results suggest that PT can act as a concise, operable and cost-efficient tool for CECs screening which has the potential to be embedded in the initial large scale screening stage for anti-counterfeit.Item Open Access A dissection and enhancement technique for combined damage characterisation in composite laminates using laser-line scanning thermography(Elsevier, 2021-05-24) Liu, Haochen; Du, Weixiang; Yazdani Nezhad, Hamed; Starr, Andrew; Zhao, YifanImpact induced combined damage in composite laminates attracts great attention due to its significant degradation of the structural integrity. However, the provision of the quantitative analysis of each damage portion is challenging due to its bare visibility and structural mixture complexity, so-called barely visible impact damage (BVID), which is referred to as inter-laminar delamination, and is inherently coupled with in-plane transverse and matrix damage also known as combined damage. Instead of focusing on one type of damage in most of the existing studies, this paper proposes a decomposition and targeted enhancement technique based on Stationary Wavelet Transform (SWT) for such coupled BVID in composite laminates using laser-line scanning thermography. Firstly, a combined damage model composed of in-plane damage and inter-laminar delamination is established by finite element numerical modelling to predict the thermal response pattern in the laser scanning thermography. Then, a feature separation and targeted enhancement strategy based on SWT in the frequency domain is proposed to improve the contrast of the matrix crack and delamination in combined damage scenarios induced by low-velocity rigid impact via drop-tower tests, meanwhile eliminating noise and suppressing the laser pattern background. The enhanced images of in-plane damage and delamination are furtherly processed by Random Sample Consensus (RANSAC) method and confidence map algorithms to calibrate the damage profile. The proposed technique is validated through inspecting a group of unidirectional carbon fibre-reinforced polymer composite samples, impacted by a variety of energy levels, in fibre-parallel (0°), 45° and orthogonal scanning modes. The results demonstrate that the proposed technique can pertinently isolate, enhance and characterise the inspected in-plane crack and inter-laminates delamination in a flexible manner. The proposed methodology paves the way towards automated infrared thermography data analysis for quantitative dissection of actual combined damage in composite laminates.Item Open Access An efficient electromagnetic and thermal modelling of eddy current pulsed thermography for quantitative evaluation of blade fatigue cracks in heavy-duty gas turbines(Elsevier, 2020-03-19) Tong, Zongfei; Xie, Shejuan; Liu, Haochen; Zhang, Weixu; Pei, Cuixiang; Li, Yong; Chen, Zhenmao; Uchimoto, Tetsuya; Takagi, ToshiyukiThe blade surface fatigue cracks often occur during service of Heavy-Duty Gas Turbines (HDGT) in high temperature, high rotational velocity and high frequency vibration environment. These fatigue cracks seriously threaten the safe operation of heavy-duty gas turbines, which would cause significant hazard or economic loss. The quantitative evaluation of blade surface fatigue cracks is extremely significant to HDGT. Eddy current pulsed thermography (ECPT) is an emerging non-destructive testing technology and show great potential for fatigue crack evaluation. This paper proposes a novel electromagnetic and thermal modelling of ECPT to achieve fast and effective quantitative evaluation for surface fatigue cracks. First, the proposed numerical method calculates electromagnetic field using the reduced magnetic vector potential method in the frequency domain based on frequency series method. The thermal source is transformed to an equivalent and simple form according to the energy equivalent method. Second, the temperature signals of ECPT are calculated through the time-domain iteration strategy with a relatively large time step. Then the ECPT experimental setup is established and the developed simulator is validated numerically and experimentally. The developed simulator is five times faster than the previous one and can be applied to eddy current thermography (ECT) with any kind of excitation waveforms. Finally, the depth of surface fatigue crack is quantitatively evaluated by means of the developed simulator, which is not only a promising simulation progress for ECPT, but also can be an effective tool embedded HDGT though-life maintenanceItem Open Access A fiber-guided motorized rotation laser scanning thermography technique for impact damage crack inspection in composites(IEEE, 2023-04-11) Liu, Haochen; Tinsley, Lawrence; Deng, Kailun; Wang, Yizhong; Starr, Andrew; Chen, Zhenmao; Zhao, YifanLaser Thermography manifests superior sensitivity and compatibility to detect cracks and small subsurface defects. However, the existing related systems have limitations on either inspection efficiency or unknown directional cracks due to the utilization of stationary heat sources. This article reports a Fiber-guided Motorised Rotation Laser-line Scanning Thermography (FMRLST) system aiming to rapidly inspect cracks of impact damage with unknown direction in composite laminates. An optical head with fibre delivery integrated with a rotation motor is designed and developed to generate novel scanning heating in a circumferential rotation manner. A FEM model is first proposed to simulate the principle of FMRLST testing and produce thermograms for the development of post-processing methods. A damage enhancement method based on Curvelet Transform is developed to enhance the visualization of thermal features of cracks, and purify the resulting image by suppressing the laser-line heating pattern and cancelling noise. The validation on three composite specimens with different levels of impact damage suggests the developed FMRLST system can extract unknown impact surface cracks efficiently. The remarkable sensitivity and flexibility of FMRLST to arbitrary cracks, along with the miniaturized probe-like inspection unit, present its potential in on-site thermographic inspection, and its design is promising to push the LST towards.Item Open Access A full 3D reconstruction of rail tracks using a camera array(Elsevier, 2023-12-14) Wang, Yizhong; Liu, Haochen; Yang, Lichao; Durazo-Cardenas, Isidro; Namoano, Bernadin; Zhong, Cheng; Zhao, YifanThis research addresses limitations found in existing 3D track reconstruction studies, which often focus solely on specific rail sections or encounter deployment challenges with rolling stock. To address this challenge, we propose an innovative solution: a rolling-stock embedded arch camera array scanning system. The system includes a semi-circumferential focusing vision array, an arch camera holder, and a Computer Numerical Control machine to simulate track traverse. We propose an optimal configuration that balances accuracy, full rail coverage, and modelling efficiency. Sensitivity analysis demonstrates a reconstruction accuracy within 0.4 mm when compared to Lidar-generated ground truth models. Two real-world experiments validate the system's effectiveness following essential data preprocessing. This integrated technique, when combined with rail rolling stocks and robotic maintenance platforms, facilitates swift, unmanned, and highly accurate track reconstruction and surveying.Item Open Access Inspection of delamination defect in first wall panel of Tokamak device by using laser infrared thermography technique(IEEE, 2018-04-16) Liu, Haochen; Pei, Cuixiang; Qiu, Jinxing; Chen, ZhenmaoFirst wall panels (FWPs), which adjoin the inner wall of the blanket modules in the vacuum vessel (VV) of a Tokamak device, are in structures of multilayer bounded together with a solid welding technique in order to perform its heat exchange, VV protection, and neutron breeding functions. The quality of the welding joint between layers is the key factor for FWP integrity. In order to conduct online inspection of the delamination defect in the FWPs, a nondestructive testing (NDT) method capable to detect delamination defect without accessing into the VV is required. In this paper, the feasibility of the laser infrared thermography (LIRT) testing NDT method was investigated experimentally for this purpose. To clarify its detectability under practical VV environment, inspections of several inspection modes were conducted based on the practical structure of FWP and VV of the EAST Tokamak device, i.e., modes of different distances and angles of FWPs toward the LIRT transducers. In practice, an LIRT testing system was established and several double-layered plate specimens with different artificial delamination defects were inspected under the selected testing conditions. Through thermography signal reconstruction, an image processing algorithm was proposed and adopted to enhance the defect detectability. From the results of different inspection modes, it was found that the angle factor may worsen the inspection precision and reduce the detectability for delamination defects in case of big defect depth-to-width ratio, even though the LIRT method is still applicable for inspection of relative large defects in FWP. Finally, the detectability in different inspection modes was clarified, which proved the feasibility of LIRT for FWP online inspection.Item Open Access Inspection of electronic component using pulsed thermography(Elsevier, 2021-07-14) Tinsley, Lawrence; Liu, Haochen; Addepalli, Sri; Lam, Wayne; Zhao, YifanCounterfeit electronic components (CEC) are of great concern to governments and industry globally as they could lead to systems and mission failure, malfunctioning of safety critical systems, and reduced reliability of high-hazard assets. Depending on the cost of CEC going into the production line, some industries might look to have some sort of inspection capability in-house to screen critical components before they go to assembly. Although advanced counterfeit inspection methods have been developed for a variety of components, they generally exhibit a combination of disadvantages such as destructive, low throughput, high unit cost, or inaccessible to unskilled operator. This paper investigates the potential of pulsed thermography on detection of CEC in a fast and non-destructive manner. The second derivative of post-heat thermal response is used to construct a fingerprint to differentiate genuine and counterfeit components. Results successfully demonstrate the potential of the proposed solution.Item Open Access Inversion technique for quantitative infrared thermography evaluation of delamination defects in multilayered structures(IEEE, 2019-10-31) Liu, Haochen; Pei, Cuixiang; Xie, Shejuan; Li, Yong; Zhao, Yifan; Chen, ZhenmaoInverse analysis is a promising tool for quantitative evaluation offering informative model-based prediction and providing accurate reconstruction results without pre-inspections for characterization criteria. For traditional defect inverse reconstruction, a large number of parameters are required to reconstruct a complex defect, and the corresponding forward modelling simulation is very time-consuming. Such issues result in ill-posed and complex inverse reconstruction results, which further reduces its practical applicability. In this paper, we propose and experimentally validate an inversion technique for the reconstruction of complexly-shaped delamination defects in a multilayered metallic structure using signals derived from infrared thermography (IRT) testing. First, we employ a novel defect parameterization strategy based on Fourier series fitting to represent the profile of a complicated delamination defect with relatively few coefficients. Secondly, the multi-medium element modelling method is applied to enhance a FEM fast forward simulator, in order to solve the mismatching mesh issue for mesh updating during inversion. Thirdly, a deterministic inverse algorithm based on a penalty conjugate gradient algorithm is employed to realize a robust and efficient inverse analysis. By reconstructing delamination profiles with both numerically-simulated IRT signals and those obtained through laser IRT experiments, the validity, efficiency and robustness of the proposed inversion method are demonstrated for delamination defects in a double-layered plate. Based on this strategy, not only is the feasibility of the proposed method in Infrared thermography NDT validated, but the practical applicability of inversion reconstruction analysis is significantly improved.Item Open Access Investigating precision and accuracy of a robotic inspection and repair system(SSRN, 2021-10-20) Rahman, Miftahur; Liu, Haochen; Rahimi, Masoumeh; Ruiz Carcel, Cristobal; Kirkwood, Leigh; Durazo-Cardenas, Isidro; Starr, AndrewRobot integration in railway maintenance steps a prominent pavement in high-efficient and low-cost job execution for the infrastructure management. To achieve practical and diverse inspection and repair railway job, a robot manipulator on a locomotive platform is one of the best options. A lot of research has been conducted to find the accuracy and precision of industrial robotic manipulator where the manipulator base is fixed. This paper initiates an exploration of the accuracy and precision of a Robotic Inspection and Repair System (RIRS), which is a novel robotic railway maintenance system integrated with an industrial manipulator (UR10e) with 6 degree-of-freedom, mounting on an Unmanned Ground Vehicle (UGV) (Warthog) and specially designed trolley. In this research, a mimic track visual inspection test using QR code detection is adopted and implemented by an arm-mounted monocular camera. Then a sequential pose moves with multiple payload weights on the manipulator end has been performed as a performance measurement of repair jobs using a vision-based position tracking algorithm. The measurement results demonstrate that RIRS can maintain accurate and consistent performance in both defect position inspection and repair moves with diverse payloads. For inspection the positional error was only 0.27% while for repair moves the end-effector can reach the same position within 1mm. This research establishes a foundation for system command & control development and supporting more practical railway jobs deployment towards full autonomy for RIRS in the future.Item Open Access Laser array spots thermography for detection of cracks in curved structures(IOS, 2020-12-10) Jinxinga, Qiu; Pei, Cuixiang; Yang, Yang; Liu, Haochen; Chen, ZhenmaoThe laser array spot thermography (LAST) is a fully non-contact and non-destructive method for the inspection of surface cracks with high efficiency. In this study, the detection capability of this method for the inspection of surface cracks in structures with curved surfaces is experimentally studied. The influence of the inspection angle on the crack imaging results is also investigated. The experiment results show that cracks in surface of the pipes with different dimeters can be detected and imaged by LAST.Item Open Access Localisation and navigation framework for autonomous railway robotic inspection and repair system(SSRN, 2021-10-20) Rahimi, Masoumeh; Liu, Haochen; Rahman, Miftahur; Ruiz Carcel, Cristobal; Durazo-Cardenas, Isidro; Starr, Andrew; Hall, Amanda; Anderson, RobertIn the path towards the intelligent industrial 4.0, the railway industry is keen to develop intelligent asset management strategies for digitalization and smart management for rail infrastructure. It aims to both reduce the cost and exposure of human-labor, associated with track maintenance risk, as well as increase the autonomy and accuracy for the railway inspection and repair job. A Robotic Inspection and Repair System (RIRS) is proposed to undertake the automated railway maintenance consisting of the autonomous off-track travel between base workshop and track, road-rail conversion, autonomous on-track inspection, and repair as well as remote communicating to railway signaling system and infrastructure system. This paper presents a localization and navigation framework for this new autonomous system; applied to the mentioned railway maintenance job. This system comprises a commercial Unmanned Ground Vehicle (UGV, named Warthog) with a robotic manipulator (UR10e), and multiple onboard sensors including Lidar, camera, RTK GNSS, IMU, wheel odometry, and multiple types of cameras. An adaptive trolley is also designed for the purpose of road-rail conversion. This research also focuses on how to increase accuracy for the support of track defect detection and localization.Item Open Access A machine learning-based clustering approach to diagnose multi-component degradation of aircraft fuel systems(Springer, 2021-10-07) Liu, Haochen; Zhao, Yifan; Zaporowska, Anna; Skaf, ZakwanAccurate fault diagnosis and prognosis can significantly reduce maintenance costs, increase the safety and availability of engineering systems that have become increasingly complex. It has been observed that very limited researches have been reported on fault diagnosis where multi-component degradation are presented. This is essentially a challenging Complex Systems problem where features multiple components interacting simultaneously and nonlinearly with each other and its environment on multiple levels. Even the degradation of a single component can lead to a misidentification of the fault severity level. This paper introduces a new test rig to simulate the multi-component degradation of the aircraft fuel system. A machine learning-based data analytical approach based on the classification of clustering features from both time and frequency domains is proposed. The scope of this framework is the identification of the location and severity of not only the system fault but also the multi-component degradation. The results illustrate that (a) the fault can be detected with accuracy > 99%; (b) the severity of fault can be identified with an accuracy of almost 100%; (c) the degradation level can be successfully identified with the R-square value > 0.9.