The use of autonomous vehicles for marine and submarine work has risen considerably in the last decade. Developing new monitoring systems, navigation and communications technologies allows a wide range of operational possibilities. Autonomous Underwater Vehicles (AUVs) are being used in offshore missions and applications with some innovative purposes by using sustainable and green energy sources. This paper considers an AUV that uses a hydrogen fuel cell, achieving zero emissions. This paper analyses the life cycle cost of the UAV and compares it with a UAV powered by conventional energy. The EN 60300-3-3 guidelines have been employed to develop the cost models. The output results show estimations for the net present value under different scenarios and financial strategies. The study has been completed with the discount rate sensibility analysis in terms of financial viability.

Latest intelligent vehicles require a higher level of reliability for controller area network (CAN). Furthermore, the spread of electric vehicles may worsen the problem of electromagnetic interference. Severe electromagnetic interference can affect the bit error rate of the frames being communicated, leading to more frequent inconsistent message omission (IMO) or inconsistent message duplication (IMD) faults, which can be attributed to defects in the retransmission mechanism of CAN. To address the problems of IMO and IMD, this paper presents a dual-channel network structure and corresponding operating protocol, named as fault-tolerant dual-channel CAN. The IMO occurrence rate is maintained at a considerably low level by employing a dual-channel CAN over which replicate messages are transmitted. At the same time, any duplicate frames caused by IMD are identified and removed by analyzing the frame reception time. The integrity of the proposed network structure and operating protocol are evaluated through formal verification.

This article introduces an augmented adaptive unscented Kalman filter (KF). The proposed novel technique is suitable to simultaneously estimate both the diagonal process noise covariance matrix and the unknown inputs, thus combining previously reported KF estimators for unknown inputs (dual or joint KF) and for covariance matrices (adaptive KF). A selective scaling method is also introduced to improve the convergence property of the suggested KF. The development of the novel KF is also motivated by a specific estimation problem related to crane systems. Cranes represent a special class of weight handling equipment as they are underactuated and described by nonlinear dynamics such that the load present oscillatory behavior. In addition to the increasing need for their automation in various industrial fields, these features also make them a benchmark system in control engineering with numerous control laws reported in the literature for sway elimination and trajectory tracking. A common issue to realize most of the advanced control laws on real, eventually industrial size cranes is the necessity to know the sway angle and frictions on the configuration variables. It is shown in simulation and also with real experiments on a reduced size overhead crane system that the suggested KF is suitable to estimate both the sway angles and the frictions.

This paper addresses the application of rotor speed signal for the detection and diagnosis of ball bearing faults in rotating electrical machines. Many existing techniques for bearing fault diagnosis (BFD) rely on vibration signals or current signals. However, vibration- or current-based BFD techniques suffer from various challenges that must be addressed. As an alternative, this paper takes the initial step of investigating the efficiency of rotor speed monitoring for BFD. The bearing failure modes are reviewed and their effects on the rotor speed signal are described. Based on this analysis, a novel BFD technique, the rotor speed-based BFD (RSB-BFD) method under variable speed and constant load conditions, is proposed to provide a benefit in terms of cost and simplicity. The proposed RSB-BFD method exploits the absolute value-based principal component analysis (PCA), which improves the performance of classical PCA by using the absolute value of weights and the sum square error. The performance and effectiveness of the RSB-BFD method is demonstrated using an experimental setup with a set of realistic bearing faults in the outer race, inner race, and balls.

The use of autonomous vehicles for marine and submarine work has risen considerably in the last decade. Developing new monitoring systems, navigation and communications technologies allows a wide range of operational possibilities. Autonomous Underwater Vehicles (AUVs) are being used in offshore missions and applications with some innovative purposes by using sustainable and green energy sources. This paper considers an AUV that uses a hydrogen fuel cell, achieving zero emissions. This paper analyses the life cycle cost of the UAV and compares it with a UAV powered by conventional energy. The EN 60300-3-3 guidelines have been employed to develop the cost models. The output results show estimations for the net present value under different scenarios and financial strategies. The study has been completed with the discount rate sensibility analysis in terms of financial viability.

Latest intelligent vehicles require a higher level of reliability for controller area network (CAN). Furthermore, the spread of electric vehicles may worsen the problem of electromagnetic interference. Severe electromagnetic interference can affect the bit error rate of the frames being communicated, leading to more frequent inconsistent message omission (IMO) or inconsistent message duplication (IMD) faults, which can be attributed to defects in the retransmission mechanism of CAN. To address the problems of IMO and IMD, this paper presents a dual-channel network structure and corresponding operating protocol, named as fault-tolerant dual-channel CAN. The IMO occurrence rate is maintained at a considerably low level by employing a dual-channel CAN over which replicate messages are transmitted. At the same time, any duplicate frames caused by IMD are identified and removed by analyzing the frame reception time. The integrity of the proposed network structure and operating protocol are evaluated through formal verification.

This article introduces an augmented adaptive unscented Kalman filter (KF). The proposed novel technique is suitable to simultaneously estimate both the diagonal process noise covariance matrix and the unknown inputs, thus combining previously reported KF estimators for unknown inputs (dual or joint KF) and for covariance matrices (adaptive KF). A selective scaling method is also introduced to improve the convergence property of the suggested KF. The development of the novel KF is also motivated by a specific estimation problem related to crane systems. Cranes represent a special class of weight handling equipment as they are underactuated and described by nonlinear dynamics such that the load present oscillatory behavior. In addition to the increasing need for their automation in various industrial fields, these features also make them a benchmark system in control engineering with numerous control laws reported in the literature for sway elimination and trajectory tracking. A common issue to realize most of the advanced control laws on real, eventually industrial size cranes is the necessity to know the sway angle and frictions on the configuration variables. It is shown in simulation and also with real experiments on a reduced size overhead crane system that the suggested KF is suitable to estimate both the sway angles and the frictions.

This paper addresses the application of rotor speed signal for the detection and diagnosis of ball bearing faults in rotating electrical machines. Many existing techniques for bearing fault diagnosis (BFD) rely on vibration signals or current signals. However, vibration- or current-based BFD techniques suffer from various challenges that must be addressed. As an alternative, this paper takes the initial step of investigating the efficiency of rotor speed monitoring for BFD. The bearing failure modes are reviewed and their effects on the rotor speed signal are described. Based on this analysis, a novel BFD technique, the rotor speed-based BFD (RSB-BFD) method under variable speed and constant load conditions, is proposed to provide a benefit in terms of cost and simplicity. The proposed RSB-BFD method exploits the absolute value-based principal component analysis (PCA), which improves the performance of classical PCA by using the absolute value of weights and the sum square error. The performance and effectiveness of the RSB-BFD method is demonstrated using an experimental setup with a set of realistic bearing faults in the outer race, inner race, and balls.

In this paper, we present a practical synthesis method using unfoldings which are based on partial order semantics and hence free of state space explosion inherently. In addition, we suggest several conditions for basic gate implementation in order to enhance practicality of the suggested method.

Physical insight into a material can be first gained by its color since the reflectance spectrum from an object reflects its microstructure and complex reflective indices. We here present a comprehensive overview of electrodynamics and optics related to reflectance spectra and color and provide an open-source Python code for simulating reflectance spectra and extracting color values. The validity and applicability of the code are demonstrated through comparative analysis with both literature and experimental data.

This paper presents a phased fault tree analysis (phased-FTA)-based approach to evaluate the performability of Autonomous Underwater Vehicles (AUVs) in real time. AUVs carry out a wide range of missions, including surveying the marine environment, searching for specific targets, and topographic mapping. For evaluating the performability of an AUV, it is necessary to focus on the mission-dependent components and/or subsystems, because each mission exploits different combinations of devices and equipment. In this paper, we define a performability index that quantifies the ability of an AUV to perform the desired mission. The novelty of this work is that the performability of the AUV is evaluated based on the reliability and performance of the relevant resources for each mission. In this work, the component weight, expressing the degree of relevance to the mission, is determined using a ranking system. The proposed ranking system assesses the performance of the components required for each mission. The proposed method is demonstrated under various mission scenarios with different sets of faults and performance degradations.

Simulation-based validation is a vital approach for evaluating the performance of algorithms in a virtual setting by inducing faults and assessing their responses. This method proves essential for cost and time savings while addressing the challenges associated with acquiring real-world data. The hardware-in-the-loop simulation (HILS) technique has emerged as a valuable tool for creating a realistic and dependable simulation environment that interacts with real systems. This approach allows flexible modification and enhancement of state monitoring algorithms. In this study, we integrate a sensor system designed for remotely monitoring industrial scrubber fan systems with an actual scrubber fan setup. The monitoring algorithm undergoes testing using the HILS technique, employing a virtual fault injection system.