Modern Data-Driven Strategies for Enhancing Reliability and Efficiency in Predictive Maintenance of Shipyard Infrastructure

Author	Objectives	Applications	Parameters used	Methodology	Outcome	Limitations
Yi et al. [34]	Examine intelligent case designs and models for shipyards.	Shipyards with intelligent factories	Case studies and smart factory specifications	Establishing frameworks and analyzing cases	established a framework for the integration of smart technology in shipbuilding	restricted to Chinese shipyards; might not be relevant in other contexts
Schwendemann et al. [35]	Examine ML methods for predictive maintenance and bearing condition monitoring.	machines for grinding	Numerous ML techniques with condition data	review of the literature and a comparison of methods	thorough review of ML methods for bearings	restricted to grinding machine bearings; might not apply to other parts
Serradilla et al. [36]	Models using DL for predictive maintenance.	Predictive general maintenance	historical maintenance data and DL models	Review of literature, comparison of models, and identification of challenges	Perspectives on DL models, obstacles, and potential paths forward	mostly concentrates on DL; it might not cover other ML methods
Ayvaz et al. [37]	Provide a manufacturing line predictive maintenance solution that operates in real time.	production lines for manufacturing	ML algorithms, real-time operational data, and IoT data	Real-time data gathering and predictive ML	Increased production line efficiency and real-time predictive maintenance	Problems with the integration and processing of real-time data
Lang et al. [38]	Use ML models using physics knowledge to forecast ship speed.	Prediction of ship speed	Ship characteristics, outside circumstances, and physics-based ML methods	combining ML and physics-based models to accelerate prediction	increased speed prediction accuracy and improved physical constraint management	Complexity of combining ML with physics-based models

[i] DL, deep learning; IoT, Internet of things; ML, machine learning.

Proposed methodology. BiLSTM-SPAN, directional long short-term memory network with spatial pyramid attention networks; DL, deep learning.

Table 2:

Operational performance of several cranes

	MH-3	MH-4	AH	LT	CT-3	CT-4
Operating hours	1.8983956	1.5413483	0.1719212	2.7106885	0.7127384	0.68275827
No of operation	533	315	49	648	290	192
Pulsating operation	112	58	2	215	39	25
Backtracking operation	6	6	1	1	10	1
Load (kN)	147.15	4,905	1,962	1,471.5	981	1,471.5
Lifting Speed (m/min)	20	30	20	40	50	60
Current (A)	100	200	150	100	300	400
Torque(Nm)	20,000	30,000	5,000	10,000	20,000	30,000
Voltage (V)	690	690	400	400	690	690
Displacement (mm)	30	40	10	30	40	45
Change in length(mm)	10	15	5	10	15	20
Noise (dB)	80	85	80	95	85	90
Vibration (Hz)	1	2	0.5	0.5	1	1.5

Structure of BiLSTM. BiLSTM, bidirectional long short-term memory; LSTM, long short-term memory network.

Crane component dimensions change prediction using BiLSTM-SPAN. BiLSTM-SPAN, directional long short terms memory network with spatial pyramid attention networks.

Voltage (V) and vibration levels (Hz) recorded for six cranes over 31 days (A) Voltage readings and, (B) vibration measurements.

Torque and dimensional changes (mm) recorded over 31 days. (A) crane torque values and (B) dimensional changes.

Noise levels (dB) and load values (kN) recorded for six cranes over 31 days. (A) Noise measurements and (B) load variations.

Current (A) and operational speed (m/min) recorded for six cranes over 31 days. (A) Current measurements and (B) speed variations.

Table 3:

Results of displacement

Days	MH-3	MH-4	AH	LT	CT-3	CT-4
1	6,215.773	9,324.361	12,297.11	14,999.12	17,161.76	18,783.39
2	8,511.442	11,079.25	15,399.62	16,347.21	18,103.97	19,859.8
3	10,536.37	12,699.25	15,393.54	17,423.62	18,909.17	20,666.17
4	12,966.48	14,181.3	12,957.13	17,148.67	17,551.5	19,175.47
5	13,637.71	12,152.39	10,789.58	15,388.63	15,249.52	18,359.51
6	11,471.57	11,068.97	12,948.01	13,493.69	16,733.44	18,084.56
7	8,088.968	8,902.588	15,510.67	14,166.33	17,945.92	19,972.02
8	6,328.23	7,276.751	17,126.93	16,054.26	19,696.84	20,912.59
9	8,890.664	9,031.411	15,227.54	17,671.91	18,878.55	19,962.67
10	11,996.45	11,055.17	13,059.76	18,612.95	17,792.08	19,281.38
11	14,019.51	13,889.75	16,298.81	17,663.26	17,248.03	18,735.46
12	13,472.42	15,371.1	16,159	16,847.77	19,136.43	20,623.62
13	10,901.33	15,365.25	13,990.75	15,762.24	20,620.35	22,105.9
14	9,277.367	12,522.96	13,039.89	17,919.74	19,939.99	21,560.45
15	11,030.16	10,627.55	11,953.66	17,780.16	18,044.82	19,799.01
16	13,863.8	13,191.63	12,894.7	16,695.57	17,230.5	18,577.18
17	13,046.67	15,621.5	16,132.35	15,338.83	16,414.07	18,705.77
18	10,069.24	13,724.92	17,072.92	15,064.12	16,544.07	20,864.43
19	9,658.225	11,422.94	15,308.67	14,519.13	18,837.16	22,075.28
20	8,300.322	11,147.99	13,681.9	15,730.91	20,050.34	21,933.59
21	10,323.62	10,330.86	13,002.72	17,753.27	21,126.05	20,849.23
22	13,563.83	10,461.56	14,348.46	18,965.05	19,500.68	19,628.34
23	13,559.16	12,486.25	15,830.28	17,475.05	18,550.29	18,947.98
24	11,932.39	14,915.42	16,500.81	16,931	17,329.16	19,889.02
25	9,900.207	15,585.96	16,361.47	16,116.68	16,649.51	20,695.86
26	8,679.31	14,231.33	14,736.33	15,301.89	18,131.79	21,231.96
27	9,755.018	12,605.02	13,112.84	14,757.38	19,074.47	20,958.19
28	12,318.62	11,519.5	14,326.95	16,644.13	19,203.29	20,684.87
29	14,068.6	10,569.11	16,218.85	17,721.24	19,198.85	20,005.69
30	12,033.85	12,728.94	17,434.14	17,439.51	19,192.07	21,759.41
31	10,270.54	12,702.52	17,432.73	18,918.76	20,405.48	22,432.52

Comparison of BiLSTM-SPAN (A) AdaBoost and (B) Gradient Boosting. AdaBoost, adaptive boosting; BiLSTM-SPAN, directional long short-term memory network with spatial pyramid attention networks; RUL, remaining useful life.

Comparison of BiLSTM-SPAN (A) MLP Regressor and (B) RF. BiLSTM-SPAN, directional long short-term memory network with spatial pyramid attention networks; MLP, multi-layer perceptron; RF, random forest; RUL, remaining useful life.

Comparison of BiLSTM-SPAN (A) SVR and (B) XGBoost. BiLSTM-SPAN, directional long short-term memory network with spatial pyramid attention networks; RUL, remaining useful life; SVR, support vector regression; XGBOOST, eXtreme gradient boosting.

Table 4:

Comparison of prediction result

Algorithm	R²	MAE	MAPE	RMSE
BiLSTM (proposed)	0.910	35.40	2.10	120.00
RF	0.892	41.87	2.37	127.17
XGBoost	0.889	71.02	4.19	147.38
Gradient boosting	0.767	285.31	33.99	432.85
MLP regressor	0.654	378.28	20.29	533.47
SVR	0.341	593.55	32.77	798.11
AdaBoost	0.327	692.89	37.23	889.45

[i] AdaBoost, adaptive boosting; BiLSTM, bidirectional long short-term memory; MAE, mean absolute error; MAPE, mean absolute percentage error; MLP, multi-layer perceptron; RF, random forest; RMSE, random mean square error; SVR, support vector regression; XGBoost, eXtreme gradient boosting.

Table 5:

Cross validation and statistical significance performances

Algorithm	R² (mean ± std)	MAE (mean ± std)	RMSE (mean ± std)	Statistical significance (p-value)
BiLSTM-SPAN (proposed)	0.910 ± 0.008	2.51 ± 0.10	0.910 ± 0.10	-
RF	0.841 ± 0.0013	3.18 ± 0.14	0.841 ± 0.17	0.013
XGBoost	0.859 ± 0.0011	3.35 ± 0.16	0.859 ± 0.19	0.016
Gradient boosting	0.878 ± 0.009	2.98 ± 0.12	0.878 ± 0.15	0.008
MLP regressor	0.865 ± 0.0010	3.12 ± 0.14	0.865 ± 0.17	0.012
SVR	0.872 ± 0.0011	3.05 ± 0.13	0.872 ± 0.16	0.010
AdaBoost	0.857 ± 0.0012	3.21 ± 0.15	0.857 ± 0.18	0.014

[i] AdaBoost, Adaptive Boosting; BiLSTM-SPAN, directional long short terms memory network with spatial pyramid attention networks; MAE, mean absolute error; MLP, multi-layer perceptron; RF, random forest; RMSE, random mean square error; SVR, support vector regression.

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DOI: https://doi.org/10.2478/ijssis-2026-0020 | Journal eISSN: 1178-5608

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Language: English

Submitted on: Aug 18, 2025

Published on: May 2, 2026

Published by: International Journal on Smart Sensing and Intelligent Systems

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

predictive maintenance,

innovative intelligent strategies,

deep learning,

machine learning,

bidirectional long short-term memory,

spatial pyramid attention network

Related subjects:

Engineering,

Introductions and overviews,

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© 2026 Vijaya Kumar Chava, Vijaya Geeta Dharmavaram, Vinil Chowdhary Chava, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 19 (2026): Issue 1 (January 2026)

Modern Data-Driven Strategies for Enhancing Reliability and Efficiency in Predictive Maintenance of Shipyard Infrastructure

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