The existing marine diesel engine fault diagnosis methods mainly have the problems of model complexity, large amount of calculation, and unable to carry out real-time fault diagnosis of diesel engine. In this paper, a simple and practical approach to detect faults of marine diesel engine is studied. According to a set of sensing data, the fitting equation of each parameter changing with the running state of diesel engine was fitted statistically. Then, the threshold range of each parameter changing with the running state of diesel engine was fitted. During fault diagnosis, the real-time parameters of the sensor in the current running state were calculated according to the real-time running data. If the parameters exceed the threshold range, it is abnormal operation. Because the sensor signal corresponds to the operation status of each specific component, the abnormal evaluation directly indicates the specific fault. Experimental results show that the method has a good practical effect.

/pdf/a-practical-approach-to-detect-faults-of-marine-diesel-1ogvwlo8m4.pdf

A Practical Approach to Detect Faults of Marine Diesel Engine

The aim of this research is to enhance the control of a Wind Energy Generating System (WEGS) incorporating a Five-phase Permanent Magnet Synchronous Generator (5 ph-PMSG) and addressing operational challenges in the presence of disturbances. The commonly employed Second-Order Super-Twisting Controller (SOSTC), with constant gains, has limitations, including uncertainties, significant transient state errors, and chattering effects, hindering its practical applicability. To overcome these drawbacks, the study proposes the application of an Adaptive Third-Order Continuous Super-Twisting Control (ATOCSTC). This novel approach integrates a third-order sliding mode surface, a smoothly continuous switching control term, and an adaptation law. Unlike the conventional SOSTC using discontinuous "sign" functions, ATOCSTC dynamically adjusts gains using tangent hyperbolic functions, providing a continuous and effective control mechanism. Comparative analysis with the conventional SOSTC under various disturbances such as wind speed fluctuations, parameter variations, and Grid Voltage Sag (GVS), reveals significant improvements with ATOCSTC. Notably, ATOCSTC leads to a 75% reduction in power ripples for grid active power and a 40.38% reduction for grid reactive power. These findings underscore the superior performances obtained with the proposed ATOCSTC in WEGS. 

A Novel Adaptive Third-Order Continuous Super-Twisting Controller of a Five Phase Permanent Magnet Synchronous Wind Generator

Software requirements are the most critical phase focused on documenting, eliciting, and maintaining the stakeholders’ requirements. Risk identification and analysis are preemptive actions designed to anticipate and prepare for potential issues. Usually, this classification of risks is done manually, a practice that the personal judgment of the risk analyst or the project manager might influence. Machine learning (ML) techniques were proposed to predict the risk level in software requirements. The techniques used were logistic regression (LR), multilayer perceptron (MLP) neural network, support vector machine (SVM), decision tree (DT), naive bayes, and random forest (RF). Each model was trained and tested using cross-validation with k-folds, each with its respective parameters, to provide optimal results. Finally, they were compared based on precision, accuracy, and recall metrics. Statistical tests were performed to determine if there were significant differences between the different ML techniques used to classify risks. The results concluded that the DT and RF are the techniques that best predict the risk level in software requirements.

Comparing machine learning techniques for software requirements risk prediction

An Overview on the Use of Machine Learning Algorithms for Identifying Anomalies in Industrial Valves

Increasing interest in natural gas-fired gensets is motivated by District Heating (DH) network applications, especially in urban areas. Even if they represent customary solutions, when used in DH, duty regimes are driven by network thermal energy demands resulting in discontinuous operation, which affects their remaining useful life. As such, the attention on effective condition-based maintenance has gained momentum. In this paper, a novel unsupervised anomaly detection framework is proposed for gensets in DH networks based on Supervisory Control And Data Acquisition (SCADA) data. The framework relies on multivariate Machine-Learning (ML) regression models trained with a Leave-One-Out Cross-Validation method. Model residuals generated during the testing phase are then post-processed with a sliding threshold approach based on a rolling average. This methodology is tested against nine major failures that occurred on the gas genset installed in the Aosta DH plant in Italy. The results show that the proposed framework successfully detects anomalies and anticipates SCADA alarms related to unscheduled downtime.

Condition-Based Maintenance of Gensets in District Heating Using Unsupervised Normal Behavior Models Applied on SCADA Data

As a kind of power supply equipment, diesel generator set has the characteristics of good mobility, fast start, stable power supply, convenient operation and maintenance. Diesel generator set is very important for power supply applications. The research on automatic fault diagnosis of diesel generator set is of great significance for monitoring the operation status of diesel generator and timely maintenance. Compared with traditional neural networks, deep believe network improves the learning efficiency of multi-layer networks by introducing restricted Boltzmann machine. A deep believe network based fault diagnosis for diesel generator set is developed. The sensor data collected from diesel generator set are processed to form a training dataset, and deep believe network is designed. The experimental results show that the deep believe network based method has the best fault diagnosis performance in recall, precision, accuracy and F1-score than other learning based methods.

Fault diagnosis of diesel generator set based on deep believe network

Abstract The internal combustion engine is a widely used power equipment in various fields, and its energy utilization is measured using brake specific fuel consumption (BSFC). BSFC map plays a crucial role in the analysis, optimization, and assessment of internal combustion engines. However, due to cost constraints, some values on the BSFC map are estimated using techniques like K-nearest neighbor, inverse distance weighted interpolation, and multi-layer perceptron, which are recognized for their limited accuracy, particularly when dealing with distributed sampled data. To address this, an improved random forest method is proposed for the estimation of BSFC. Polynomial features are employed to increase higher dimensions of features for random forest by nonlinear transformation, and critical parameters are optimized by particle swarm optimization algorithms. The performance of different methods was compared on two datasets to estimate 20%, 30%, and 40% of BSFC data, and the results reveal that the method proposed in this paper outperforms other common methods and is suitable for estimating the BSFC map. 

Random forest method for estimation of brake specific fuel consumption

This paper studies an improved super-twisting sliding mode controller (IST-SMC) for the permanent magnet synchronous generator (PMSG) voltage loop to improve the anti-disturbance capability of the system. Compared to conventional voltage controllers, the control algorithm provides advantages in terms of system resistance to load disturbances. Conventional voltage controllers have significant voltage fluctuations and long recovery times during sudden load changes. To solve this problem, a voltage loop controller based on a super-twisting sliding mode (ST-SMC) is designed to enhance the immunity of the system. Also, the ST-SMC was improved to further increase the convergence rate of the system and enhance the dynamic performance. The convergence of the system away from the balance point is accelerated by introducing an exponential term, which in turn provides an improvement in the dynamic performance of the system. The effectiveness of the proposed control scheme was verified on a PMSG. 

A Second-Order Sliding Mode Voltage Controller with Fast Convergence for a Permanent Magnet Synchronous Generator System

Integrated power systems are gaining popularity in the field of power systems and DC integrated power systems are considered promising for electric propulsion ships due to their simple grid topology, low fuel consumption, and easy access to new energy sources. However, the dynamic response characteristics of the power plant can be compromised when a variable speed generator is used in a DC power system, despite achieving energy savings. In this research, we investigate the power control strategy of a specific type of a ferry’s DC power plant. We establish a mathematical model and a Matlab/Simulink-based simulation model to analyze the performance of the proposed strategy. The research utilizes the fast charging and discharging advantages of supercapacitor storage devices to compensate for the dynamic impact delay of the power output when using the variable speed generator set. Additionally, an improved DC bus voltage droop control method that incorporates voltage compensation is proposed to mitigate problems related to large bus voltage fluctuations under sudden load change conditions, enabling better load distribution between different power sources. The simulation results confirm the effectiveness of the proposed strategy in optimizing the speed-seeking method of the variable speed diesel engine sets matching with the supercapacitor, and its positive impact on the dynamic performance of the propulsion system is demonstrated under variable load conditions resulting from ferry operations.

/pdf/power-control-strategy-for-a-ferrys-dc-power-system-using-30r03d6w.pdf

Power Control Strategy for a Ferry’s DC Power System Using Supercapacitors

A marine variable speed generator can dynamically adjust the set speed of a diesel engine according to the actual power load level by the grading method, so the goal of energy conservation is achieved. However, the grading method relies on engineering experience. The actual range of power load fluctuation is uncertain, which leads to a decrease in fuel efficiency. Therefore, a method based on load sliding interval is put forward. In this method, a fixed width load interval is set at first, and then the interval slides on the diesel engine load at a step of 1%. The minimum fuel consumption rate speed for the interval is optimized during the sliding process. Finally, a minimum fuel consumption rate speed curve corresponding to the sliding interval position is obtained. The optimization objective is to have the minimum weighted average fuel consumption rate at the integer load points within each interval. The actual power load is monitored and used to determine the current load interval during the calculation of the set speed. The minimum fuel consumption rate speed curve optimized was applied to query the setting value of the diesel speed. The mean value engine model was applied for simulation under three load conditions. The results show that the sliding interval method could achieve the best fuel saving effect under any power load; the set value of the diesel engine speed changed smoothly under the fluctuating power load conditions, and the fuel saving ability was better according to the actual power load conditions on the ship. 

Research on Speed Optimization and Adjusting Strategy of Variable Speed Diesel Generator Base on Sliding Interval

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