Grey Relational Analysis-Based Fault Prediction for Watercraft Equipment

Feng, Shasha; Chen, Zijian; Guan, Qunsheng; Yue, Jingtao; Xia, Chengyi

doi:10.3389/fphy.2022.885768

ORIGINAL RESEARCH article

Front. Phys., 25 May 2022

Sec. Interdisciplinary Physics

Volume 10 - 2022 | https://doi.org/10.3389/fphy.2022.885768

Grey Relational Analysis-Based Fault Prediction for Watercraft Equipment

Shasha Feng ¹

Zijian Chen ¹

Qunsheng Guan ¹

Jingtao Yue ¹

Chengyi Xia ²^*

1. Institute of Military Transportation, Army Military Transportation University, Tianjin, China
2. School of Control Science and Engineering, Tiangong University, Tianjin, China

Article metrics

View details

Citations

1,4k

Views

557

Downloads

Abstract

In this study, we aim to investigate the fault prediction for watercraft equipment by using grey relational analysis. At first, the healthy degree model of watercraft equipment is proposed, and then two main theorems are derived to determine the health condition criteria for equipment. Lastly, the relevant simulation results are provided to verify the validity and accuracy of the healthy degree model. Current results can be helpful to effectively design the supporting mode of watercraft equipment and realize the transformation of watercraft equipment support from planned maintenance to predictive maintenance.

1 Introduction

Maintenance task affects the reliability and availability of equipment, which is the key factor to minimize failure time and reduce operation cost in the lifecycle of equipment [1]. Currently, the majority of methods of equipment maintenance are planned maintenance [2–10]. Among them, AKYUZ and CELIK designed an enhanced planned maintenance system (E-PMS) for a ship by using A’WOT, and their study had made a great contribution to improving the performance of equipment [2]. In practice, however, the planned maintenance is greatly influenced by the external environment and heavily depends on human effort, which leads to low efficiency and poor accuracy in the following two aspects: one is excessive maintenance, which means the unnecessary maintenance of better equipment, and the other one is insufficient maintenance, and the equipment has broken down before the maintenance period due to various reasons, but restricted by the maintenance plan, it has to operate with faults.

Therefore, fault prediction is the key to realizing the transformation of support mode from planned maintenance to predictive maintenance, which can perform the warning before the failure of equipment occurs. Also, more and more attention has been focused on fault prediction for equipment [11–22], such as fault prediction for the vehicle [11, 12], the watercraft [13–15], the aircraft engine [16–18], the power supply system [19, 20], and the track circuit [21–23]. Due to the complexity of watercraft structure and the diversity of the marine environment, it is challenging and difficult to study the fault prediction for watercraft equipment.

Over the past few years, a large number of methods were explored to predict the failure, such as the grey model [

], the BP neural network [

], the RBF neural network [

], the data-driven model [

–

], deep learning [

], and the grey relational analysis method [

]. Although the grey models in [

] were effective to a certain extent, they only considered the development of a single or several characteristic parameters independently. There were also some results that focused on theoretical research and had made some contributions [

]. But their experimental data was generated from the simulation, which cannot represent the data characteristics of a real physical system. Meanwhile, a data-driven prediction method was introduced into the failure prognosis of marine diesel engines, and a discrete wavelet transform was applied to process the data based on the data characteristics [

]. In order to take multiple characteristic parameters into account comprehensively, the grey neural network model was first introduced into fault prediction for ship machinery in [

]. However, most of the existing works [

] only analyze whether the watercraft equipment will break down; they cannot explain the type of the fault and when the fault will occur. In response to the aforementioned problems, the healthy degree model based on grey relational analysis of watercraft equipment is proposed in this study, and it has been applied to a certain type of watercraft and the improvement of performance has been approved. The highlights of this study are summarized as follows.

∗ A novel healthy degree model is put forward by using grey relational analysis. The healthy state of watercraft equipment can be predicted by the value of the healthy degree. If the healthy degree is greater than 1, then the watercraft equipment will be healthy. If the healthy degree is less than 1, then the watercraft equipment will break down, the fault mode will be identified, and the fault occurrence time will be predicted.
∗ The implementation of the support mode transformation from planned maintenance to predictive maintenance can solve the three major problems: whether the watercraft equipment needs to be repaired, what kind of fault it is, and when the fault will occur.

The rest of this article is arranged as follows: at first, we describe the problem to be resolved in Section 2, which consists of the necessary notations and the data generation method. Then, the healthy degree model is introduced in Section 3 to predict whether the watercraft equipment needs to be repaired, what kind of equipment fault it is, and when the fault will occur. In Section 4, an example of fault prediction is provided for the engine equipment in a certain type of watercraft, and the simulation results are obtained to verify the validity and accuracy of the fault prediction. Finally, some concluding remarks are made to end this study.

2 Problem Descriptions

2.1 Notations

The dataset to be tested is expressed as , where is an m-dimensional column vector. is defined as the normalized vector which consists of the average values of every parameter in the healthy or fault state. j = 0 denotes that y_j is a normalized vector of healthy state. If j ≠ 0, then y_j is a normalized vector of jth failure mode. represents the fault prediction curve of jth failure.

2.2 The Data Generation Method of Grey Relational Analysis

Since the range of watercraft equipment character parameters are different, and the values of them are not in one order of magnitude, it is very important to process the values into a comparability sequence. In fact, this processing is similar to normalization which is called data generation of grey relational analysis.

The method in [39] is used in this section since the data generation is obtained according to the attributes of character parameters.

If we wish to maximize the value of character parameter, then the value generated can be described as follows:which is provided in [39].

If we wish to minimize the value of character parameter, then the value generated can be denoted as follows:which is exhibited in [39].

If we wish that the value be close to the desired value y*, then the value generated can be expressed as follows:which is represented in [39].

It is obvious that the values of character parameters are transformed into the same interval . Then the healthy degree model based on grey relational analysis will be proposed in the next section.

3 Healthy Degree Model Based on Grey Relational Analysis

The traditional grey relational coefficient used in [39] is calculated merely depending on the difference between two sequences. Actually, the area can represent the grey relational coefficient between two sequences; more obviously, the larger the area is, the smaller the grey relational coefficient will be. Then the area between the two sequences labeled x_i and y_j is described as follows:where l is the number of intersections between the two sequences, and intersections are expressed as . The similarity whose monotonicity is opposite to the area between two sequences is structured as follows:

After that, the grey relational coefficient is represented as follows:where S_ij and δ_ij are the area and similarity between the two sequences, respectively. Clearly, the grey relational coefficient increases with the increase of similarity, while it decreases when the area is increased.

Finally, the healthy degree is expressed as follows:

If all of h_ij is greater than 1 for any and , then the watercraft equipment is in a healthy state. If there exists any h_ij smaller than 1 for and , then the watercraft equipment will break down, and the failure mode can be identified as the jth failure mode.

Theorem 1

: For , , if and , then .

Proof: According to Eq., the following equations can be obtained:From the known condition , we have . On account of which the function is a decreasing function, then Eq. can be obtained:Based on Eq., Eq. can be transformed into . Because the function is a decreasing function, Eq. can be acquired:In the same way, Eq. can be deduced:On the basis of Eq. and Eq., we can obtainThen, we have , that is,By virtue of which the function is a decreasing function, then Eq. can be described as follows:Multiplying both sides of Eq. by the same positive number , we have , that is, . Henceforth, Theorem 1 can be proved.

Theorem 2

: If r_ij < r_i0 for and , then the equipment will be healthy . If there exists r_ij > r_i0 for and , then the equipment will break down .

Proof: Due to r_ij < r_i0 for and , then we can getOn account of which the function is a decreasing function, then Eq. can be changed as follows:Based on Eq., we can obtain h_ij > 1, that is, the equipment will be healthy.With the same method, if there exists r_ij > r_i0 for and , then the equipment will be judged to break down. Thus, Theorem 2 can be proved.

4 Simulation Results

A fault prediction example is given for the engine equipment in a certain type of watercraft. Three kinds of common faults are chosen to establish the healthy degree model. Fault 1 is excessive clearance of the crankpin bearing or main bearing, fault 2 is cooling water leakage, and fault 3 is propeller overload. Parameters of cylinder temperature (labeled CT), oil pressure (labeled OP), oil temperature (labeled OT), freshwater pressure (labeled FWP), freshwater temperature (labeled FWT), and exhaust temperature (labeled ET) are selected as the character parameters. According to their values of them in the states of health and three failure modes, the average value of them in each state is considered as the normalized vector which is described in Table 1 where P is the parameter and NV is the normalized vector.

TABLE 1

P	NV
P	Normal	Fault 1	Fault 2	Fault 3
CT	450.20	448.50	435.40	495.20
OP	0.24	0.15	0.22	0.25
OT	58.50	45.60	72.30	75.80
FWP	0.083	0.082	0.074	0.080
FWT	67.50	65.00	82.40	85.60
ET	426.30	430.20	419.70	500.00

Normalized vector table in the states of health and three failure modes.

Taking time as the horizontal axis and healthy degree as the vertical axis, three kinds of fault prediction curves are represented to analyze the trend of the curve and predict the occurrence time of the fault included. In this case, the time period is 100 s with a failure point included. The corresponding historical failure record is exhibited in Table 2. If the predicted results obtained by the healthy degree model are consistent with Table 2, then the effectiveness of the healthy degree model can be verified.

TABLE 2

Fault mode	Fault occurrence time/s
Fault 1	64.23
Fault 2	58.93
Fault 3	89.96

Historical failure record of which the time period is 100 s with the failure point included.

In the process of the engine equipment from normal operation to the failure of excessive clearance of the crankpin bearing or main bearing, the time period 100 s including the failure point is studied, and the data are collected every 10 s. The collected data are shown in Table 3, where P and T mean the parameter and time.

TABLE 3

T/s	P
T/s	CT	OP	OT	FWP	FWT	ET
10	457.28	0.22	62.51	0.089	64.37	420.43
20	459.05	0.25	63.84	0.083	65.46	425.45
30	454.10	0.27	65.39	0.088	67.82	424.10
40	455.82	0.14	48.46	0.089	68.43	440.04
50	448.69	0.20	45.87	0.085	67.693	430.54
60	453.31	0.21	42.96	0.079	64.20	439.48
70	443.95	0.12	44.30	0.082	69.95	431.71
80	444.03	0.14	44.62	0.081	64.03	413.44
90	440.44	0.16	48.76	0.081	61.50	432.52
100	455.38	0.15	43.23	0.083	65.45	430.16

Collected data of engine equipment from normal operation to fault 1.

The generated data aforementioned are brought into the healthy degree model to calculate the healthy degree exhibited in Table 4, where HD is healthy degree and T is time, and the graph of fault prediction curves is displayed in Figure 1.

TABLE 4

T/s	HD
T/s	H₁	H₂	H₃
10	1.0627	1.0524	1.0847
20	1.0986	1.1010	1.1236
30	1.1515	1.1381	1.1706
40	1.0270	1.0433	1.0535
50	1.0151	1.1004	1.1181
60	1.0036	1.0311	1.0407
70	0.9944	1.0368	1.0550
80	0.9928	1.0201	1.0400
90	0.9880	1.0285	1.0465
100	0.9645	1.0498	1.0624

Healthy degree of engine equipment from normal operation to fault 1.

FIGURE 1

Fault prediction curves of engine equipment from normal operation to fault 1. It is obvious that only the healthy degree of fault prediction curve of fault 1 starts to be less than 1 in the time period from 60 to 70 s, and it decreases with time.

It is obvious that only the healthy degree of fault 1 starts to be less than 1 in the time period from 60 to 70 s, and it decreases with time. In other words, the failure of excessive clearance of the crankpin bearing or main bearing will occur in the time period from 60 to 70 s, which is consistent with Table 2.

In the process of engine equipment from normal operation to fault 2, in this case, the time period is 100 s with a failure point included, and the data are collected every 10 s which is described in Table 5.

TABLE 5

T/s	P
T/s	CT	OP	OT	FWP	FWT	ET
10	461.44	0.22	59.92	0.091	67.74	418.83
20	465.80	0.28	55.65	0.081	67.36	437.16
30	458.57	0.25	61.26	0.084	69.93	436.04
40	452.73	0.28	61.84	0.082	70.13	412.68
50	427.16	0.22	60.08	0.092	66.05	411.70
60	435.81	0.24	71.46	0.065	68.08	428.93
70	448.64	0.29	72.35	0.072	83.40	449.87
80	468.27	0.28	72.60	0.064	84.70	421.75
90	446.62	0.30	74.84	0.052	85.60	422.67
100	432.84	0.22	82.26	0.072	88.60	423.46

Collected data of engine equipment from normal operation to fault 2.

Based on the collected data and the healthy degree model, the healthy degree is obtained in Table 6, and the graph of fault prediction curves is shown in Figure 2.

TABLE 6

T/s	HD
T/s	H₁	H₂	H₃
10	1.1063	1.0927	1.1222
20	1.0590	1.0661	1.0750
30	1.0580	1.0576	1.0727
40	1.0622	1.0482	1.0758
50	1.0085	1.0151	1.0458
60	1.0325	0.9945	1.0646
70	1.0134	0.9649	1.0113
80	1.0167	0.9594	1.0128
90	1.0225	0.9461	1.0170
100	1.0052	0.9325	1.0093

Healthy degree of engine equipment from normal operation to fault 2.

FIGURE 2

Fault prediction curves of engine equipment from normal operation to fault 2. It is evident that only the fault prediction curve of fault 2 whose healthy degree starts to be less than 1 in the time period from 50 to 60 s.

According to Figure 2, only the fault prediction curve of the leakage of cooling water whose healthy degree starts to be less than 1 in the time period from 50 to 60 s, and the healthy degree decreases with time. Therefore, the fault of the leakage of cooling water will happen in the time period from 50 to 60 s, which is in line with Table 2.

The time period of 100 s with the failure point included is discussed in the process of engine equipment from normal operation to fault 3. The data are collected every 10 s, as shown in Table 7.

TABLE 7

T/s	P
T/s	CT	OP	OT	FWP	FWT	ET
10	459.49	0.27	53.55	0.088	75.40	427.18
20	453.80	0.29	64.92	0.081	67.10	446.90
30	442.14	0.30	55.29	0.087	67.13	426.98
40	445.92	0.26	53.76	0.084	65.94	421.26
50	465.42	0.25	63.57	0.083	70.44	438.60
60	465.79	0.24	60.26	0.080	70.50	452.26
70	476.66	0.22	61.60	0.083	65.51	463.03
80	467.84	0.29	62.53	0.084	69.79	474.96
90	477.45	0.22	66.69	0.088	67.64	482.24
100	466.68	0.24	59.45	0.083	74.81	496.52

Collected data of engine equipment from normal operation to fault 3.

The collected data are used to calculate the healthy degree in Table 8, and the graph of fault prediction curves is displayed in Figure 3.

TABLE 8

T/s	HD
T/s	H₁	H₂	H₃
10	1.0616	1.0602	1.0798
20	1.0459	1.0455	1.0573
30	1.1771	1.1923	1.2240
40	1.0815	1.0860	1.1190
50	1.0311	1.0284	1.0391
60	1.0248	1.0243	1.0283
70	1.0098	1.0141	1.0106
80	1.0118	1.0111	1.0056
90	1.0130	1.0122	0.9989
100	1.0097	1.0084	0.9910

Healthy degree of engine equipment from normal operation to fault 3.

FIGURE 3

Fault prediction curves of engine equipment from normal operation to fault 3. It is obvious that only the fault prediction curve of fault 3 whose healthy degree starts to be less than 1 when the time is close to 90 s, and the healthy degree decreases with time.

It is evident that only the fault prediction curve of the propeller overload whose healthy degree starts to be less than 1 when the time is close to 90 s, and the healthy degree decreases with time. In short, the occurrence time of the propeller overload failure will be close to 90 s, which matches the data shown in Table 2.

5 Conclusion

In summary, the healthy degree model of watercraft equipment is proposed in this article. On the basis of grey relational analysis, the healthy degree model can solve three major problems: whether watercraft equipment needs to be repaired, what kind of fault it is, and when the fault will occur. After that, we analytically derive two theorems related to the healthy degree model, which are conducive to comprehending and applying the healthy degree model. Finally, the real data of the engine equipment in a certain type of watercraft are utilized and the relevant simulation results are provided to verify the effectiveness of the healthy degree model, and there are failure data of three types of common faults which are excessive clearance of the crankpin bearing or main bearing, the leakage of cooling water, and the propeller overload. Obviously, the predicted results of the healthy degree model are consistent with reality. The current analysis of fault prediction will be beneficial to change the support mode of watercraft equipment and realize the transformation of watercraft equipment support from planned maintenance to predictive maintenance.

In addition, there still remains a disadvantage due to the lack of failure data in this study. We will further study the generation of failure data via establishing the simulation model of watercraft equipment, carrying out the fault simulation experiment, and fully utilizing the data science in [40, 41].

Statements

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.

Author contributions

SF: writing the original manuscript. SF, ZC, and QG: healthy degree model analysis. SF: simulation experiment. SF and JY: revising the manuscript. CX: review, guidance, and editing. All authors have made efforts for the work and agreed to its publication.

Funding

This work was partially supported by the Project of Prognostic and Health Management (Grant 47201) and the National Natural Science Foundation of China (NSFC) (Grant 61773286).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1.
LazakisIRaptodimosYVarelasT. Predicting Ship Machinery System Condition through Analytical Reliability Tools and Artificial Neural Networks. Ocean Eng (2018) 152:404–15. 10.1016/j.oceaneng.2017.11.017
- CrossRef
- Google Scholar
2.
AkyuzECelikM. Using of A'wot to Design an Enhanced Planned Maintenance System (E-Pms) On-Board Ship. Brod (2017) 68:61–75. 10.21278/brod68104
- CrossRef
- Google Scholar
3.
GašparGPoljakIOrovićJ. Computerized Planned Maintenance System Software Models. Pomor (Online) (2018) 32:141–5. 10.31217/p.32.1.14
- CrossRef
- Google Scholar
4.
StanivukTStazićLVidovićFČobanovA. Ship Planned Maintenance System Data Analysis. Int J Traffic Transp Eng (2020) 10:432–6. 10.7708/ijtte.2020.1002
- CrossRef
- Google Scholar
5.
Al-TurkiUDuffuaaSBendayaM. Trends in Turnaround Maintenance Planning: Literature Review. Jqme (2019) 25:253–71. 10.1108/jqme.2017.007410.1108/jqme-10-2017-0074
- CrossRef
- Google Scholar
6.
StazićLKomarIMihanovićLMišuraA. Shipowner′s Impact on Planned Maintenance System Database Quality Grades Resemblance Equalization. Trans Marit Sci (2018) 7:5–22. 10.7225/toms.2018.07.001
- CrossRef
- Google Scholar
7.
KimMHyunDKKimSHJiWKKwonKS. A Study on the Scheduling of Planned Maintenance for Multicomponent System with Hidden Failures: Focusing on Inspection Cost. J Korea Academia-Industrial Coop Soc (2019) 20:149–58. 10.5762/kais.2019.20.10.149
- CrossRef
- Google Scholar
8.
Fernández-MarcaDMostacero-RojasKNúñez-PonceVRaymundoCMamani-MacedoNMoguerzaJM. Lean Manufacturing Model of Production Management under the Focus on Maintenance Planned to Improve the Capacity Used in a Plastics Industry Sme. In: International Conference on Applied Human Factors and Ergonomics. Springer (2020). 448–54. 10.1007/978-3-030-50791-6_57
- CrossRef
- Google Scholar
9.
Jasiulewicz-KaczmarekM. Swot Analysis for Planned Maintenance Strategy-A Case Study. IFAC-PapersOnLine (2016) 49:674–9. 10.1016/j.ifacol.2016.07.788
- CrossRef
- Google Scholar
10.
WangW. A Stochastic Model for Joint Spare Parts Inventory and Planned Maintenance Optimisation. Eur J Operational Res (2012) 216:127–39. 10.1016/j.ejor.2011.07.031
- CrossRef
- Google Scholar
11.
NabweyHA. A Method for Fault Prediction of Air Brake System in Vehicles. Int J Eng Res Technol (2020) 13:1002–8. 10.37624/ijert/13.5.2020.1002-1008
- CrossRef
- Google Scholar
12.
ShafiUSafiAShahidARZiauddinSSaleemMQ. Vehicle Remote Health Monitoring and Prognostic Maintenance System. J Adv Transp (2018) 2018:1–10. 10.1155.2018.806151410.1155/2018/8061514
- CrossRef
- Google Scholar
13.
TraVKimJ. Pressure Vessel Diagnosis by Eliminating Undesired Signal Sources and Incorporating ga-based Fault Feature Evaluation. IEEE Access (2020) 8:134653–67. 10.1109/access.2020.3010871
- CrossRef
- Google Scholar
14.
SahuARPaleiSK. Fault Prediction of Drag System Using Artificial Neural Network for Prevention of Dragline Failure. Eng Fail Anal (2020) 113:104542–29. 10.1016/j.engfailanal.2020.104542
- CrossRef
- Google Scholar
15.
TsaganosGNikitakosNDalaklisDÖlcerAIPapachristosD. Machine Learning Algorithms in Shipping: Improving Engine Fault Detection and Diagnosis via Ensemble Methods. WMU J Marit Aff (2020) 19:51–72. 10.1007/s13437-019-00192-w
- CrossRef
- Google Scholar
16.
ShangLLiuG. Sensor and Actuator Fault Detection and Isolation for a High Performance Aircraft Engine Bleed Air Temperature Control System. IEEE Trans. Contr. Syst. Technol. (2011) 19:1260–8. 10.1109/tcst.2010.2076353
- CrossRef
- Google Scholar
17.
LiuYWangTZhangHCheutetV. Simulation-based Fuzzy-Rough Nearest Neighbour Fault Classification and Prediction for Aircraft Maintenance. J Simul (2021) 15:202–16. 10.1080/17477778.2019.1680261
- CrossRef
- Google Scholar
18.
ZhaoY-PWangJ-JLiX-YPengG-JYangZ. Extended Least Squares Support Vector Machine with Applications to Fault Diagnosis of Aircraft Engine. ISA Trans (2020) 97:189–201. 10.1016/j.isatra.2019.08.036
- CrossRef
- Google Scholar
19.
ZhangSWangYLiuMBaoZ. Data-based Line Trip Fault Prediction in Power Systems Using Lstm Networks and Svm. IEEE Access (2018) 6:7675–86. 10.1109/access.2017.2785763
- CrossRef
- Google Scholar
20.
FengDLinSHeZSunX. A Technical Framework of Phm and Active Maintenance for Modern High-Speed Railway Traction Power Supply Systems. Int J Rail Transp (2017) 5:145–69. 10.1109/access.2017.278576310.1080/23248378.2017.1286954
- CrossRef
- Google Scholar
21.
HuL-QHeC-FCaiZ-QWenLRenT. Track Circuit Fault Prediction Method Based on Grey Theory and Expert System. J Vis Commun Image Represent (2019) 58:37–45. 10.1016/j.jvcir.2018.10.024
- CrossRef
- Google Scholar
22.
de BruinTVerbertKBabuškaR. Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks. IEEE Trans. Neural Netw. Learn Syst. (2017) 28:523–33. 10.1109/tnnls.2016.2551940
- CrossRef
- Google Scholar
23.
LiuSWeiYYinYFengTLinJ. Structural Health Monitoring Method of Pantograph-Catenary System Based on Strain Response Inversion. Front. Phys. (2021) 9:316. 10.3389/fphy.2021.691510
- CrossRef
- Google Scholar
24.
Shiuh-Jer HuangSJChien-Lo HuangCL. Control of an Inverted Pendulum Using Grey Prediction Model. IEEE Trans. Ind. Appl. (2000) 36:452–8. 10.1109/28.833761
- CrossRef
- Google Scholar
25.
LiuWZhangMWangY. Weak Thruster Fault Prediction Method for Autonomous Underwater Vehicles Based on Grey Model. In: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 233 (2019). 348–56. 10.1177/0959651818793730
- CrossRef
- Google Scholar
26.
YiJWangQZhaoDWenJT. Bp Neural Network Prediction-Based Variable-Period Sampling Approach for Networked Control Systems. Appl Math Comput (2007) 185:976–88. 10.1016/j.amc.2006.07.020
- CrossRef
- Google Scholar
27.
WongWEQiY. Bp Neural Network-Based Effective Fault Localization. Int. J. Soft. Eng. Knowl. Eng. (2009) 19:573–97. 10.1142/s021819400900426x
- CrossRef
- Google Scholar
28.
Ke MengKZhao Yang DongZYDian Hui WangDHKit Po WongKP. A Self-Adaptive Rbf Neural Network Classifier for Transformer Fault Analysis. IEEE Trans. Power Syst. (2010) 25:1350–60. 10.1109/tpwrs.2010.2040491
- CrossRef
- Google Scholar
29.
ChaiWQiaoJ. Non-linear System Identification and Fault Detection Method Using Rbf Neural Networks with Set Membership Estimation. Ijmic (2013) 20:114–20. 10.1504/ijmic.2013.056183
- CrossRef
- Google Scholar
30.
PanYMaoZXiaoQHeXZhangY. Discrete Wavelet Transform Based Data Trend Prediction for Marine Diesel Engine. In: 6th Data Driven Control and Learning Systems (DDCLS). IEEE (2017). 782–7.
- Google Scholar
31.
BaptistaMSankararamanSde MedeirosIPJrNascimentoCPrendingerH. Forecasting Fault Events for Predictive Maintenance Using Data-Driven Techniques and Arma Modeling. Comput Industrial Eng (2018) 115:41–53. 10.1016/j.cie.2017.10.033
- CrossRef
- Google Scholar
32.
OdgaardPFLinBJorgensenSB. Observer and Data-Driven-Model-Based Fault Detection in Power Plant Coal Mills. IEEE Trans Energy Convers (2008) 23:659–68. 10.1109/tec.2007.914185
- CrossRef
- Google Scholar
33.
ZhaoXKimJWarnsKWangXRamuhalliPCetinerSet alPrognostics and Health Management in Nuclear Power Plants: an Updated Method-Centric Review with Special Focus on Data-Driven Methods. Front Energy Res (2021) 9:294. 10.3389/fenrg.2021.696785
- CrossRef
- Google Scholar
34.
SheJShiTXueSZhuYLuSSunPet alDiagnosis and Prediction for Loss of Coolant Accidents in Nuclear Power Plants Using Deep Learning Methods. Front Phys (2021) 9:1–9. 10.3389/fenrg.2021.665262
- CrossRef
- Google Scholar
35.
LiuHCYouJXFanXJLinQL. Failure Mode and Effects Analysis Using D Numbers and Grey Relational Projection Method. Expert Syst Appl (2014) 41:4670–9. 10.1016/j.eswa.2014.01.031
- CrossRef
- Google Scholar
36.
YinSDingSXHaghaniAHaoHZhangP. A Comparison Study of Basic Data-Driven Fault Diagnosis and Process Monitoring Methods on the Benchmark tennessee Eastman Process. J Process Control (2012) 22:1567–81. 10.1016/j.jprocont.2012.06.009
- CrossRef
- Google Scholar
37.
WangXQiLChenCTangJJiangM. Grey System Theory Based Prediction for Topic Trend on Internet. Eng Appl Artif Intell (2014) 29:191–200. 10.1016/j.engappai.2013.12.005
- CrossRef
- Google Scholar
38.
YangGWuX. Fault Prediction of Ship Machinery Based on Grey Neural Network Model. In: 2007 IEEE International Conference on Control and Automation (IEEE) (2007). 1063–6.
- Google Scholar
39.
KuoYYangTHuangGW. The Use of Grey Relational Analysis in Solving Multiple Attribute Decision-Making Problems. Comput Industrial Eng (2008) 55:80–93. 10.1016/j.cie.2007.12.002
- CrossRef
- Google Scholar
40.
LiHWangLBuZCaoJShiY. Measuring the Network Vulnerability Based on Markov Criticality. ACM Trans Knowl Discov Data (TKDD) (2021) 16:1–24. 10.1145/3464390
- CrossRef
- Google Scholar
41.
LiHXuWSongSWangWPercM. The Dynamics of Epidemic Spreading on Signed Networks. Chaos, Solit Fractals (2021) 151:111294. 10.1016/j.chaos.2021.111294
- CrossRef
- Google Scholar

Summary

Keywords

watercraft equipment, fault prediction, grey relational analysis, healthy degree model, predictive maintenance

Citation

Feng S, Chen Z, Guan Q, Yue J and Xia C (2022) Grey Relational Analysis-Based Fault Prediction for Watercraft Equipment. Front. Phys. 10:885768. doi: 10.3389/fphy.2022.885768

Received

28 February 2022

Accepted

15 April 2022

Published

25 May 2022

Volume

10 - 2022

Edited by

Xiaojie Chen, University of Electronic Science and Technology of China, China

Reviewed by

Huijia Li, Beijing University of Posts and Telecommunications (BUPT), China

Song Cheng, Hefei University of Technology, China

Updates

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Chengyi Xia, xialooking@163.com

This article was submitted to Interdisciplinary Physics, a section of the journal Frontiers in Physics

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Interdisciplinary Physics

ORIGINAL RESEARCH article

Grey Relational Analysis-Based Fault Prediction for Watercraft Equipment

Abstract

1 Introduction