Injectors play a crucial role in optimal combustion of marine diesel generator engines, but their performance degradation often causes exhaust gas temperature fluctuations that reduce efficiency and operational reliability. This study aims to analyze the effect of injector performance on exhaust gas temperature on the SV. STELLA 28 vessel. Using a causal descriptive quantitative approach, the population is operational data of Auxiliary Engine 1 and 2 for 12 months, with a purposive sample of 70 observations. Instruments include injector maximum pressure (PMAX) measurements and exhaust gas temperature thermocouples, analyzed through the Kolmogorov-Smirnov normality test, simple linear regression, and SPSS. The results show a significant effect (Sig. 0.000) with R² 0.417 and the equation . The conclusion states that optimal injector performance stabilizes exhaust gas temperature, supporting condition-based maintenance.

Performance-based warranties are widely used, but existing warranties have not adequately considered the customer’s need. Therefore, we propose a novel metric, performance availability, which represents the equipment’s ability to maintain a high-level performance. We integrate this metric into the terms of the warranty contract and propose a condition-based maintenance policy for the manufacturer. We find that: (i) The warranty policy proposed in this paper is beneficial to customers. As the performance availability threshold increases, the average availability and average performance availability both increase accordingly. (ii) In the warranty scenario of this paper, the optimal policy under dynamic PM thresholds is a control-limit policy that outperforms traditional policy under a fixed PM threshold in reducing the manufacturer’s expected maintenance cost. (iii) Overall, the PM thresholds at each inspection point increase with the rise of the performance threshold and each PM cost, but decrease as the performance availability threshold and the penalty cost per unit time increase.

Condition-based maintenance planning for K-out-of-N systems under partial observability

To address the practical engineering challenges of limited fault samples for high-voltage circuit breaker spring operating mechanisms and the inability of single features to fully reflect equipment status, this paper proposes a small-sample fault diagnosis method based on multi-source feature fusion and Stacking ensemble learning. First, a multi-source sensing system containing MEMS (Micro-Electro-Mechanical System) pressure and travel, coil, and motor current was constructed to achieve comprehensive monitoring of the mechanical and electrical states of a 220 kV circuit breaker; in particular, the introduction of non-invasive MEMS sensors effectively solves the difficulty of capturing static spring fatigue characteristics inherent in traditional methods. Second, a high-dimensional feature space was constructed using Savitzky–Golay filtering and physical feature extraction techniques. To address the characteristics of small-sample data distribution, a two-layer Stacking ensemble learning model based on 5-fold cross-validation was designed. This model utilizes the SVM (Support Vector Machine), RF (Random Forest), and KNN (K-Nearest Neighbors) as base classifiers and Logistic Regression as the meta-learner, achieving an adaptive fusion of the advantages of heterogeneous algorithms. True-type experimental results show that the average diagnostic accuracy of this method under normal conditions and four typical fault conditions reaches 96.1%, which is superior to single base models (the RF was 94.2%). Feature importance analysis further confirms that closing and opening pressures are the most critical features for distinguishing mechanical faults. This study provides effective theoretical basis and technical support for condition-based maintenance of high-voltage circuit breakers under small-sample conditions.

Lubricating oil plays a critical role in the operation and longevity of internal combustion engines, particularly in diesel-powered urban buses. Monitoring its degradation and contamination offers valuable insights into engine condition, enabling the adoption of Condition-Based Maintenance (CBM) strategies. This study applied multivariate statistical techniques - specifically Principal Component Analysis (PCA) and K-Means clustering - to a dataset of in-service oil samples from a fleet using Lukoil 10W40. The objective was to identify distinct patterns of oil degradation associated with operational conditions and maintenance profiles. Four operational clusters were identified, including: urban-use buses with frequent idling and stop-start cycles; new engines in the break-in phase with high levels of wear metals; mature engines under regular operating conditions; and an outlier bus affected by oil leakage and extreme contamination. The results highlight those conventional indicators like mileage not totally reliable indicators of oil degradation, reinforcing the need for condition-based monitoring using physicochemical and contamination variables.

Forklifts are the main equipment in the goods management system in warehouses that have an important role in increasing productivity, operational efficiency, and smooth logistics flows. If the performance of the forklift decreases, there will be an increase in downtime, more expensive maintenance costs, and the risk of disruption in operations. This study aims to analyze the influence of Condition-Based Maintenance (CBM), Operational Environment Conditions (OEC), and Human Resource Competency (HRC) on heavy equipment performance, namely Heavy Equipment Performance (HEP), in forklift operations in warehouses. Forklift performance is measured through indicators such as transportation productivity, downtime frequency, and maintenance cost efficiency. Meanwhile, CBM, OEC, and HRC are represented by various technical, environmental, and workforce competency indicators. This study uses a quantitative approach with a causal-explanatory design. Data were collected through a Likert Scale-based questionnaire survey of 49 respondents, consisting of operators, technicians, and supervisors, using purposive sampling methods. Data analysis was carried out using the Structural Equation Modeling–Partial Least Squares (SEM-PLS) method using SmartPLS 4.0 software. The test results showed that all indicators met the criteria of validity and reliability. An R-square value of 0.783 indicates that CBM, OEC, and HRC are simultaneously able to account for 78.3% variation in forklift performance. The hypothesis test showed that CBM had a positive and significant effect on HEP with path coefficients of 9,225, followed by HRC of 5,999 and OEC of 4,073. These findings say that CBM is a major factor in improving forklift performance, followed by human resource competence and operational environmental conditions. This study concludes that improving condition-based maintenance strategies, developing operator capabilities, and improving the working environment in warehouses in an integrated manner can significantly and sustainably improve forklift performance.

Effective operations and maintenance (O&M) in modern production and service systems hinges on a careful orchestration of economic and degradation dependencies across a fleet of assets. While the economic dependencies are well studied, incorporating degradation dependencies and their impact on system operations remain an open challenge. In this article, we consider a general operations & maintenance problem, where a fleet of assets are utilized to satisfy the demand. Assets undergoing maintenance are temporarily unavailable for production. The degradation rate is influenced by two primary decision-dependent factors. First, higher production rates lead to faster degradation of assets. Second, a degrading asset can accelerate the degradation of related assets in the system. To address these dependencies, we model condition-based production and maintenance decisions for multi-asset systems with degradation interactions. We provide the first O&M model to optimize O&M in multi-asset systems with embedded decision-dependent degradation interactions. We formulate robust optimization models that inherently capture degradation and failure risks by embedding degradation signals via a set of constraints, and building condition-based uncertainty sets to model probable degradation scenarios. Our approach offers a seamless integration of data-driven degradation modeling and mathematical programming to bridge the gap across predictive and prescriptive models. Performance of the proposed O&M model is evaluated through extensive experiments, where the degradation is represented using vibration-based readings from a rotating machinery system. The proposed model provides significant improvements in terms of operation, maintenance, and reliability metrics.

Purpose In an increasingly competitive market, equipment availability is a strategic variable for the competitiveness and success of companies. The objective of the research in this article is to present contributions to reduce unplanned production stoppages and optimise the operational efficiency of an injection moulding machine. This will be achieved by developing a systematic strategy to integrate predictive and condition-based maintenance systems with maintenance management software. Design/methodology/approach The model developed is based on the continuous monitoring of electrical signals and vibrations, with the processing of data collected in real time through a script developed in Python. This integrates the information into the maintenance management software, facilitating a quick and accurate response to component wear conditions. The methodology employed was action research, as it was a case study developed in a real context, with active participation in development and implementation, with the aim of continuous improvement. Findings In August, a substantial increase was observed in the primary indicators: The mean time between failures (MTBF) increased by 97.36%, the mean time to repair (MTTR) increased by 313.31%, and the downtime was reduced by 65.04%. In December, although the figures were more moderate, significant improvements were maintained: The MTBF increased by 20%, the MTTR increased by 84%, and the downtime was reduced by 79%. Originality/value The findings of the study indicated that the implementation of a structured approach for the acquisition and monitoring of electrical signals and vibration data was imperative to achieve substantial gains.

Maritime transport in semi-enclosed seas is increasingly exposed to short-term weather variability, a challenge expected to intensify under climate change and to affect the economic sustainability of shipping operations. This study proposes an integrated probabilistic framework to assess the impact of weather-induced uncertainty on operational costs, using a ferry service in the Baltic Sea as a case study. The approach combines a semi-Markov process, representing transitions between discrete weather hazard states derived from ERA5 reanalysis data (2010–2025), with a state-dependent cost model of key technical subsystems across the vessel’s operational cycle. The results show a strongly disproportionate cost structure, with most expenditures concentrated in open-sea navigation states. Although severe weather conditions occur infrequently, they generate a nonlinear amplification of operational costs, significantly reducing cost predictability and system resilience. The findings indicate that enhancing sustainability in maritime transport requires targeted, state-specific adaptation measures, such as weather-aware routing and condition-based maintenance. The proposed framework supports climate-adaptive decision-making and contributes to sustainability-oriented planning in maritime transport through improved operational robustness and cost resilience under weather uncertainty.

Abstract The train control system is a critical piece of equipment in the field of high-speed railway signaling. Timely and comprehensive mastery of its health status, shifting daily maintenance from breakdown maintenance to condition-based maintenance, plays a vital role in guaranteeing safe train operation and improving equipment life. A CNN-Transformer encoder-BiLSTM serial-parallel neural network is proposed to predict and evaluate the health status of train control equipment. First, the collected multi-dimensional degradation status information is subjected to multi-scale local feature extraction by a Convolutional Neural Network (CNN). The improved Transformer encoder module performs feature extraction through parallel multi-head attention. By introducing three different attention mask mechanisms into the Transformer encoder, it focuses on data at different positions respectively to extract their correlations. Secondly, the Bidirectional Long Short-Term Memory (BiLSTM) module extracts, memorizes, and processes the degradation information with positional encoding input to the Transformer encoder to extract the correlation of historical sequences, thereby improving prediction accuracy. Meanwhile, an improved particle swarm optimization algorithm is introduced to establish dynamic nonlinear inertia weights to find the optimal hyperparameter combination of the model. Finally, the regression layer outputs the predicted value of the Health Index (HI) percentage. The model is capable of conducting real-time and accurate health status prediction within the entire service life of the equipment, which will assist the ground maintenance site in timely maintaining equipment triggering health warnings, thereby ensuring train operation safety while significantly reducing maintenance workload and financial costs. Experiments indicate that the prediction accuracy of the model reaches over 96%, providing a new perspective for the field of health status prediction based on deep learning models, and possessing important reference value for the safe maintenance of train control equipment and the assurance of train safe operation.

Winding damage is one of the most common and highly destructive faults in power transformers. To analyze the winding force and deformation under short-circuit conditions, this paper establishes a three-dimensional simulation model of a 220 kV oil-immersed power transformer. The force distribution of the windings under different short-circuit scenarios is investigated, and the vulnerable locations in different simulation model configurations are identified. The effects of variations in spacer blocks and tie bar quantities, as well as differences in material parameters of each component, on the evolution of weak-force regions are summarized. Finally, the influence of short-circuit cumulative effects on the maximum winding deformation is studied, providing a theoretical basis for transformer condition-based maintenance and fault prediction.

Passenger transport companies have often been affected by fires in their vehicles, causing considerable damage. As a result, it is important to study the causes and effects of these fires, as well as to define the maintenance policies and strategies to be implemented to minimize the probability of this type of accident occurring. The support for this paper was based on the study of an accident that occurred in Portugal involving a passenger bus that suffered a fire in the engine compartment, which spread to the passenger compartment and caused the destruction of the vehicle, with no personal injuries. This study used infrared image analysis technology, oil ignition temperature analysis, maintenance history, accident history and operator interviews to determine the possible cause of the ignition. It was found that the cause was due to oil leaks from the engine compartment cooling system. The present communication will share a set of explanatory elements of the circumstances in which the accident occurred. In addition to identifying the causes of the accident, the study warns of the importance of more effective and efficient maintenance, particularly when using Condition Based Maintenance (CBM), including periodic visual inspections of the various mechanical and electrical components that make up the vehicles. The conclusions presented in the study also show that these events are not unrelated to the poor or even non-existent maintenance policy for the entire fleet, including the applicable standards.

Robust condition-based generation maintenance: Balancing operations and start/stop cycling to control asset degradation rates

Port cranes are very important facilities in the contemporary port and are used 24 hours working under heavy loads and adverse environmental conditions. Any unforeseen breakdown may lead to severe issues like delays of the vessels,loss of safety,and financial damage.The conventional methods of maintenance such as reactive and preventive maintenance are no longer adequate to handle the increasing demands of the port operations.The article provides an AI-based,closed, digital maintenance system that combines the digital twin technology with Computerized Maintenance Management Systems (CMMS) and Enterprise Resource Planning (ERP) systems. The proposed framework allows the efficient and condition-based maintenance by constantly monitoring the health of cranes, automating maintenance activities, and improving the system through feedback on maintenance. The strategy aids in minimizing unplanned downtimes, enhancing the use of resources as well as facilitating dependable port activities.

The increasing complexity and safety-critical nature of aircraft engines have made effective health monitoring and predictive maintenance essential in modern aviation systems. Turbofan engines operate under extreme conditions, where unexpected component degradation can lead to serious failures, high maintenance costs, and operational downtime. Traditional maintenance strategies, such as reactive and preventive maintenance, are no longer sufficient to handle these challenges efficiently. As a result, data-driven approaches based on machine learning and deep learning have gained significant attention in prognostics and health management (PHM) applications. This review focuses on health monitoring and remaining useful life (RUL) prediction of turbofan engines using advanced deep learning techniques. It examines how sensor data collected from aircraft engines can be utilized to detect degradation patterns and estimate future engine health. Particular emphasis is placed on hybrid deep learning models that combine feature extraction and temporal sequence learning, such as convolutional neural networks, autoencoders, and long short-term memory networks. The study also discusses publicly available benchmark datasets, especially the NASA C-MAPSS dataset, which is widely used for validating RUL prediction models. Key challenges such as data imbalance, noise, model interpretability, and real-time deployment are highlighted. Overall, this review demonstrates that deep learning-based prognostic models play a crucial role in improving prediction accuracy, enhancing aviation safety, and enabling reliable condition-based maintenance strategies for turbofan engines.

Rail transport is central to achieving sustainable and energy-efficient mobility, and its digitalization is accelerating the adoption of condition-based maintenance (CBM) strategies. However, existing maintenance practices remain largely reactive or rely on limited rule-based diagnostics, which constrain safety, interoperability, and lifecycle optimization. This survey provides a comprehensive and structured review of Artificial Intelligence techniques applied to the preventive, predictive, and prescriptive maintenance of railway infrastructure. We analyze and compare machine learning and deep learning approaches-including neural networks, support vector machines, random forests, genetic algorithms, and end-to-end deep models-applied to parameters such as track geometry, vibration-based monitoring, and imaging-based inspection. The survey highlights the dominant data sources and feature engineering techniques, evaluates the model performance across subsystems, and identifies research gaps related to data quality, cross-network generalization, model robustness, and integration with real-time asset management platforms. We further discuss emerging research directions, including Digital Twins, edge AI, and Cyber-Physical predictive systems, which position AI as an enabler of autonomous infrastructure management. This survey defines the key challenges and opportunities to guide future research and standardization in intelligent railway maintenance ecosystems.

Rotary machines have a natural process of creating vibrations, which makes the important parts of the machine worn out, especially bearings and gears which will end up causing failure of the system. It is commonly accepted that Vibration analysis is the most popular diagnostic tool that is currently used to examine the condition of machinery and offer suggestions on maintenance policy. The last element of proactive maintenance strategies is condition-based maintenance (CBM), which maximizes the availability of machines by applying timely interventions and reducing expensive breakdowns. This study project seeks to develop an extensive system that will facilitate the assessment of the working condition of the rotary equipment and the most significant parts of it, particularly focus on bearings. The two are interdependent as a wellmaintained machine will need less care and a machine in the process of degradation needs quick action taken. The rotary machines are very important in many industrial pr ocesses and thus find heavy challenges due to bearing problems. The difficulties cause significant disturbances to the production process and increase the cost of maintenance. The paper at hand investigates the effectiveness of CBM as a possible remedy of the abovementioned problems. CBM is a maintenance technique that makes use of real-time machine information to make informed decisions on what to do concerning the maintenance. Through this strategy, maintenance tasks would be implemented at the most appropriate time, which would be the most efficient and effective. CBM is a planning method that enables the businesses to take a proactive initiative to counter the failure, enhance maintenance schedules, and general efficiency of the business by properly estimating the remaining periods of usage of machine parts. The research study is also adding knowledge to the existing knowledge on CBM and it provides useful insights on predictive maintenance, and its potential of improving the reliability and efficiency of rotary machinery.

Abstract Arc-erosion faults in fretting electrical contacts may pose a serious threat to the reliability of electrical connectors. However, accurately evaluating arc-erosion severity online, particularly identifying weak arc erosion, remains challenging. To address this problem, continuous wavelet transform (CWT) scalograms were employed to capture the nonstationary time-frequency characteristics of vibration signals. The CWT scalograms were further integrated with a visual geometry group (VGG) convolutional neural network to develop an intelligent arc-erosion severity evaluation method, termed CWT-VGG. Experimental results indicate that CWT scalograms offer markedly stronger discriminative power for arc-erosion severities than time-domain vibration signals. Notably, compared with five other representative methods, the proposed CWT-VGG method yields the highest average evaluation accuracy (97.03%) and the most stable performance across repeated trials. This study holds significant value for advancing condition-based early maintenance of electrical connectors.

Public assets play a strategic role in supporting governmental operations and public service delivery at the local government level. The condition and performance of public assets are crucial in ensuring operational sustainability. However, in practice, public asset maintenance is often reactive and insufficiently integrated into long-term development planning. This study aims to analyze public asset maintenance strategies and their role in supporting the operational sustainability of local governments. A qualitative approach with a descriptive-analytical design was employed. Data were collected through in-depth interviews, field observations, and documentation studies involving local government agencies responsible for asset management. Data analysis was conducted using qualitative techniques, including data reduction, data display, and conclusion drawing. The findings indicate that public asset maintenance strategies are predominantly corrective, while preventive and condition-based maintenance have not been optimally implemented. Key constraints include limited policy support, weak institutional coordination, budget constraints, insufficient human resource capacity, and underutilization of asset information systems. The study concludes that well-planned, preventive, and integrated asset maintenance strategies are essential to maintaining asset performance and ensuring the operational sustainability of local governments.

Optimal mobile condition-based maintenance with adaptive outsourcing