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Deep learning (DL) has been widely used to promote the development of intelligent fault diagnosis, bringing significant performance improvement. However, most of the existing methods cannot capture the temporal information and global features of mechanical equipment to collect sufficient fault information, resulting in performance collapse. Meanwhile, due to the complex and harsh operating environment, it is difficult to extract fault features stably and extensively using single-source fault diagnosis methods. Therefore, a novel hierarchical vision transformer (NHVT) and wavelet time–frequency architecture combined with a multi-source information fusion (MSIF) strategy has been suggested in this paper to boost stable performance by extracting and integrating rich features. The goal is to improve the end-to-end fault diagnosis performance of mechanical components. First, multi-source signals are transformed into two-dimensional time and frequency diagrams. Then, a novel hierarchical vision transformer is introduced to improve the nonlinear representation of feature maps to enrich fault features. Next, multi-source information diagrams are fused into the proposed NHVT to produce more comprehensive presentations. Finally, we employed two different multi-source datasets to verify the superiority of the proposed NHVT. Then, NHVT outperformed the state-of-the-art approach (SOTA) on the multi-source dataset of mechanical components, and the experimental results show that it is able to extract useful features from multi-source information.

The use of stress–strain analysis in structural design or mechanical components is critical for avoiding or investigating structural failures. In the case of complicated designs, mathematical full-field stress modeling produces imprecise predictions. Experimental analysis can be used as a replacement for mathematical modeling, but with the use of currently available strain gauges, it is cumbersome and impossible in the case of moving parts. Mechanoluminescent materials transform mechanical energy into visible light and can be used as a replacement for strain gauges to monitor strain/stress. Three-dimensional printing technology has made major advances in terms of additive manufacturing. In this article, we describe a method to produce an ML 3D print. The fabricated samples are precise and versatile and satisfy the need for easy and non-destructible spatial stress analysis. A 3D printed photopolymer sample with SrAl2O4: Eu, Dy particle addition only to the final layers was tested, and the number of layers was optimized. It was determined that the optimal number of layers for easy detection is in the range of 10 to 20 layers. It opens the possibility for the real-time evaluation of complex uneven forces on complex parts, thus having a good potential for commercialization. [ABSTRACT FROM AUTHOR]

Search and retrieval remains a major research topic in several domains, including computer graphics, computer vision, engineering design, etc. A search engine requires primarily an input search query and a database of items to search from. In engineering, which is the primary context of this paper, the database consists of 3D CAD models, such as washers, pistons, connecting rods, etc. A query from a user is typically in the form of a sketch, which attempts to capture the details of a 3D model. However, sketches have certain typical defects such as gaps, over-drawn portions (multi-strokes), etc. Since the retrieved results are only as good as the input query, sketches need cleaning-up and enhancement for better retrieval results. In this paper, a deep learning approach is proposed to improve or clean the query sketches. Initially, sketches from various categories are analysed in order to understand the many possible defects that may occur. A dataset of cleaned-up or enhanced query sketches is then created based on an understanding of these defects. Consequently, an end-to-end training of a deep neural network is carried out in order to provide a mapping between the defective and the clean sketches. This network takes the defective query sketch as the input and generates a clean or an enhanced query sketch. Qualitative and quantitative comparisons of the proposed approach with other state-of-the-art techniques show that the proposed approach is effective. The results of the search engine are reported using both the defective and enhanced query sketches, and it is shown that using the enhanced query sketches from the developed approach yields improved search results. [Display omitted] • The first learning-based strategy to clean rough query sketches of 3D CAD models • Introduces SketchCleanNet — an end-to-end image translation scheme • SketchCleanNet aims to understand the mapping between rough sketches and clean query images • A novel scheme to calculate the loss is introduced • Dataset Contribution: The resulting enhanced query sketch dataset is made available publicly. • This paper will significantly contribute to the research community and give researchers opportunities to develop new algorithms for search and retrieval of 3D mechanical components. [ABSTRACT FROM AUTHOR]

Abstract This paper aims to employ the Stokes number to mark aerodynamic penalties of airfoils caused by particles, which is crucial to implement their scaling experiments in a gas‐solid flow. Each Stokes number is associated with a series of approximate aerodynamic penalties which are obtained at the same Reynolds number with different chord lengths. In the process, two phenomena which go against the mark are discussed and their mechanisms are revealed. The results show they occur at smaller and larger ranges of Stokes number, respectively. The effect of gravity is the leading cause and a larger chord length or diameter particle can strength its effect. The application scope of the mark, meanwhile, is ascertained. A chord length smaller than 1.5 m is advised at Re = 1.00 × 106 and those at other Reynolds numbers are also presented. As Stokes number decreases from 3, the aerodynamic penalty increases exponentially first at the advised chord length and reaches up to their peak values at the Stokes number of 0.050. In addition, the change of the aerodynamic penalty with Stokes number is dominated by the momentum exchange in the direction of gravity in the region between the particle injection plane and the airfoil.

Abstract Precise braking pressure control of a pneumatic electric braking system (PEBS) poses challenging non‐linear control problems, since it can operate in several distinct discrete processes (the pressure increasing, holding and decreasing) by switching the on‐off solenoid valves and the piston of relay valve moves irregularly. This article describes the development and experimental validation of a viable controller for an electronically controlled braking pressure based on the theory of model predictive control (MPC). The controller design consists of two parts. The non‐linear characteristic is first studied to ensure the pressure response of PEBS when the opening time is 4–10 ms in a fixed period, followed by calculation and prediction of the optimal on‐off time of inlet valve and release valve based on the current braking pressure to realize steady state. A logic rule is designed to ensure that the MPC controller and feedback control work coordinate to guarantee engineering reliability. A comprehensive system model is derived to help characterize system non‐linearities and design the MPC predictive model. Simulations and experimental results are presented finally to show how the MPC and feedback control strategy can be successfully applied to solve the braking pressure control of a PEBS in a systematic way.

Abstract In this paper, neural network‐based data fusion is used to detect fault and isolate stator winding short circuit, outer bearing race, and broken rotor bar defects in an induction motor. In addition, the robustness of the proposed method against the disturbance introduced by the coupled pump's unbalanced power source and dry running is investigated. First, three‐phase current and voltage signals are separated by means of independent component analysis (ICA), then extracted features are combined by adopting neural networks, and finally, the system's health condition is evaluated. Experimental results indicate that data fusion based on neural networks can evaluate with high reliability the system's health condition and provide better robustness in the presence of disturbances.

Abstract Violent waves in ocean sometimes cause huge shocks to the oscillating buoy and cause its damage. Improving the structure of buoy is a common method to reduce the impact of water on buoy. This paper focuses on how to reduce the peak impact force and improve the survivability of oscillating buoy in ocean environment. Considering the air layer between oscillating buoy and water surface, the impact of buoy into water is taken as the research point in the paper. The corresponding mathematical model is established, and the analysis shows that the air cushion can greatly reduce the peak impact force of buoy. The finite element model of buoy is established, and the constant velocity entry of a series of buoys with different air cushion structures is calculated. First of all, the air cushions with different shapes are compared, the qualitative analysis shows that the air cushion of cylindrical shape has the excellent anti‐impact performance. Then, the air cushion structure with different volume was analysed, and the influence of different air cushion volume on the peak impact force of buoy entering water was quantified. It is concluded that the effect of reducing peak of air cushion increases with the increase of air cushion volume. Furthermore, a test bench for the impact of buoy into water was set up for experiments, and similar conclusions were obtained. Besides, it is found that the improvement of the impact resistance of air cushion slows down as the volume of air cushion increases, and there is an upper limit which is positively correlated with the impact velocity of buoy.

Abstract Mooring systems are a significant capital cost of a floating wave energy converter and their premature failure negatively impacts operational costs. Excessive peak loads and accumulated fatigue damage can lead to failure, so these factors are cost drivers in wave energy converter design. Here, the potential to reduce platform motion and mooring loads through modification of the power take‐off (PTO) control are investigated. An approximate velocity tracking control strategy is implemented with a linear quadratic regulator design method using differential weighting of system states. It is demonstrated that the controller can be tuned to capture similar mean power to an optimally tuned, passively damped system while significantly reducing mooring line cyclic loading. The relative accumulated fatigue damage in the mooring lines in a high energy sea‐state is found to be reduced by between 43% and 92% as a result of using the approximate velocity tracking control strategy.

Abstract A novel external buoy type wave energy device with hydraulic conversion system used for navigation lighted buoys, named floating external double buoys wave energy device, is put forward and investigated by numerical calculations and model experiments. The hydrodynamic performance of the device under regular waves is numerically calculated based on linear potential flow theory and boundary element method. The generalized modal method is used to solve the hydrodynamic problems of multi‐buoy with hinged constraints. The model experiments are carried out in a 2D wave tank with a depth of 0.9 m. The wave height is set to 1/40 of the wavelength. The influence of wave period and damping loads on the hydrodynamic performance of the device is tested. The results of numerical calculations and model experiments have shown that the appropriate selection of hydraulic damping coefficient is of great significance to improve the capture width ratio of the device, and this device has good capture performance in a certain wave range, and it is expected to effectively solve the problem of continuous power supply for middle and small types of navigation lighted buoys.

Abstract Marine renewable energy as a power source for ocean observation applications has the potential to allow longer deployment operations due to the consistent, higher, and denser energy available from this resource. This additionally could encourage deployments in remote locations where maintenance is costly or resource availability is low if dependent on solar power. More importantly, gaps in spatial data could be filled. This paper examines the feasibility of a modular horizontal pendulum wave energy converter to power National Data Buoy Center's Self‐Contained Ocean Observations Payload (SCOOP) off the coast of Washington State, U.S. The effect on power output was studied when the pendulum's radius arm, mass, and power take‐off damping were varied. Results using Matlab toolbox WEC‐Sim revealed positive correlation between radius arm length and mass to power output, where power maximised for optimal damping values. Seasonal trends in power were not significant, where a 20 kg pendulum mass was needed to meet the SCOOP base power requirement of 5 W throughout the year.

Abstract Wave energy emulators provide an option to extend studies of wave energy converters (WECs) in an electric power laboratory facility and to evaluate the performance of controllers in an experimental set‐up. In addition, the integration of control strategies in different steps of the energy conversion process is an important step to improve the WEC overall behaviour that has not yet been widely discussed. This paper proposes a real‐time emulator and a hierarchical control scheme for a hyperbaric WEC consisting of floating bodies, hydropneumatic storage system (HSS), hydraulic turbine, and doubly fed induction generator (DFIG). The proposed emulator is based on a typical electric power laboratory facility, where a numerical model reproduces the dynamics of the wet subsystem and a hardware is the WEC electrical subsystem. Thereby, a squirrel‐cage induction motor (SCIM) is coupled shaft‐to‐shaft with the DFIG. The SCIM reproduces the characteristics of torque and speed that would be observed in the real system, and then, the DFIG is subjected to a primary drive with same dynamic characteristics of the real plant. The hierarchical control integrates the generator and HSS controllers considering the optimal operating pressure for local sea state conditions. Experimental results illustrate the performance of the emulator and controllers.

Abstract In China, the bracket of underground high voltage cable transmission line is mainly made of various carbon steels. However, no unified standard exists for type selection of cable brackets in practice. Here, a selection method of the bracket based on life cycle cost assessment is proposed from the viewpoint of technical economy, in which the size of brackets, steel grade, carbon steel corrosion and eddy current loss are considered as influencing factors to the costs. Stress performance analysis under identical pole arm stress constraints is employed to determine the size and grade of carbon steel which influences the initial cost. And 3D coupled electromagnetic fluid‐dynamical and thermal finite‐element analysis is applied to calculate the eddy current loss of cable brackets which influences the operation cost. Meanwhile, multi‐physics coupling analysis and experiment are conducted to prove the correctness of loss simulation calculation. Tafel curve measured by electrochemical corrosion experiment is used to obtain the corrosion rate of carbon steel which influences the maintenance cost by affecting service life of brackets. A case study is demonstrated, and the results show that the channel steel type is the most economical for the commendatory and the proposed method is effective.

Abstract Recent research shows that braking of vehicles equipped with a front brake light is identified significantly earlier than braking of vehicles without front brake lights. Moreover, the absence of front brake lights leads to more conservative road crossing decisions. These results suggest that front brake lights are able to facilitate the pedestrians’ anticipation of dangerous traffic situations, thereby increasing road safety. The present research investigated the effects of front brake lights in the real traffic of the Berlin Tegel Airport airside. A total of 102 vehicles were equipped with front brake lights and circulated in airport traffic for a period of three and a half months. Before and after this test period, 197 staff members were asked about their experiences with and their attitude towards front brake lights. The results show that front brake lights rarely led to misunderstandings, whereas they were significantly more often perceived to facilitate communication avoiding dangerous situations. The attitude towards front brake lights was already positive at the first interview and improved significantly during the measurement period. Overall, a great majority of participants stated that front brake lights improve communication between road users and thus increase road safety.

Abstract Pneumatic brake systems are crucial for the operation of trains. However, due to switching characteristics of on/off solenoid valves, the precise pressure control and low switching activities of valves are difficult to guarantee simultaneously during braking. To address the issue, a hybrid model predictive control (MPC) method is proposed for implementing the multi‐objective optimisation in this paper, i.e. the precise pressure tracking and the valve switching reduction. In order to model the hybrid behaviour caused by continuous dynamics of the pressure and discrete features of solenoid valves, the mixed logic dynamical system theory is applied. Then, the braking control problem is recast as a hybrid MPC problem, which jointly optimises the pressure tracking precision and the valve switching. A mixed integer linear program is formulated to solve the MPC problem in an efficient way. Both simulation and experiment results show that the accurate pressure control and low switching activities of valves can be achieved by the proposed method when compared with existing methods.

Abstract Induction motors play a major role in the industry nowadays due to their simple construction, uncomplicated maintenance, and cost efficiency. As the motor operates for repeated hours, some faults may occur and, depending on the process sensitivity, can cause significant losses to the industrial production. In this context, an alternative method is proposed to detect and classify bearing faults using acoustic emission signals generated by the machines and simple features obtained from them. A pair of condenser microphones was used to acquire these signals and audio feature extraction is performed to obtain time and frequency patterns to characterize healthy and faulty machines. The major differences of the acoustic signals regarding the fault frequency signatures are discussed by analyzing specific peaks observed in their spectra. Several coupled load values and levels of power supply voltage unbalance were considered in the experimental tests, which emulate common situations encountered in industrial environments, obtaining accuracy rates over 97% of success. Finally, a comparison is presented of machine learning techniques for bearing faults classification under different load values and voltage unbalance levels.

Abstract A new model reference adaptive system based estimation technique for vector control of real and reactive power of a brushless doubly fed reluctance generator without a shaft position sensor is proposed. The rotor speed is being precisely observed in a closed‐loop manner by eliminating the error between the measured and estimated inverter‐fed (secondary) winding current angles in a stationary frame. Contrary to the existing model reference adaptive system observer designs reported in the brushless doubly fed reluctance generator literature, the reference model is entirely parameter‐free and only utilises direct measurements of the secondary currents. Furthermore, the current estimates coming from the adaptive model are obtained using the measured voltages and currents of the grid‐connected (primary) winding, which has provided prospects for much higher accuracy and superior overall performance. The realistic simulations, preliminary experimental results, and the accompanying parameter sensitivity studies have shown the great controller potential for typical operating conditions of variable speed wind turbines with maximum power point tracking.

The use of stress–strain analysis in structural design or mechanical components is critical for avoiding or investigating structural failures. In the case of complicated designs, mathematical full-field stress modeling produces imprecise predictions. Experimental analysis can be used as a replacement for mathematical modeling, but with the use of currently available strain gauges, it is cumbersome and impossible in the case of moving parts. Mechanoluminescent materials transform mechanical energy into visible light and can be used as a replacement for strain gauges to monitor strain/stress. Three-dimensional printing technology has made major advances in terms of additive manufacturing. In this article, we describe a method to produce an ML 3D print. The fabricated samples are precise and versatile and satisfy the need for easy and non-destructible spatial stress analysis. A 3D printed photopolymer sample with SrAl2O4: Eu, Dy particle addition only to the final layers was tested, and the number of layers was optimized. It was determined that the optimal number of layers for easy detection is in the range of 10 to 20 layers. It opens the possibility for the real-time evaluation of complex uneven forces on complex parts, thus having a good potential for commercialization.

Abstract Since electric vehicle with in‐wheel motor has advantages in aspects of vehicle dynamics control and energy‐efficient control, it has been widely applied in the vehicle platoon in the highway. This paper proposes an optimization and control structure that integrates ecological cooperative adaptive cruise control (CACC) for the vehicle platoon, and path tracking and ecological control allocation (Eco‐CA) for individual vehicle to improve the energy efficiency of electric vehicles in traffic flow. First, in the upper layer, the suitable driving mode is selected by finite‐state machine (FSM) based on surrounding environment. Then the path‐planner and ecological CACC in the higher layer are proposed to determine the desired path and desired speed and acceleration profile. After that, in the middle layer, longitudinal motion controller and yaw stability controller are integrated to achieve the desired control targets from upper layer. Finally, in the lower layer, each in‐wheel motor torque is optimized by Eco‐CA subsystem to reduce the energy consumption of driving motor and tyre friction loss. The simulation results demonstrate that the proposed control strategy can improve the energy efficiency and dynamics control performance.

Abstract Transient stability of a multi‐unit hydroelectric generating system significantly influences the safety operation of power stations. The uncertainty of operating parameter is one of the important factors to influence the system stability during the transient process. For this reason, the target of this paper aims to explore the optimised operating parameter in order to enhance the stability of a multi‐unit hydroelectric generating system. To overcome the limitations of existing methods in literature, a new high‐dimensional, complex and non‐linear dynamic model of the multi‐unit hydroelectric generating system that combines the conventional hydro‐turbine governing system and the shaft system is established. Then, the dynamic behaviour of the proposed system targeting the transient process is exhaustingly investigated with the change of the critical parameters. The calm interval of critical parameters is also determined in order to improve the transient stability of the system. The obtained results not only enrich the theoretical knowledge of transient dynamics but also ensure the safety operation of hydropower stations. Meanwhile, the presented research method, high‐dimensional non‐linear dynamic model and the interesting findings in transient process can promote the development of the theory of non‐linear dynamics.

Abstract This paper proposes an adaptive fuzzy fault‐tolerant control method about seat active suspension systems. The significant indexes of seat active suspension system are ride comfort and safety. Considering the actuator failure of seat active suspension system, the problems of passengers' ride comfort and safety are better solved. Two adaptive laws are designed to solve the two unknown coefficients in the actuator fault of the seat active suspension system. The fuzzy logic systems are applied to approximate the unknown terms in the system, and an adaptive fuzzy fault‐tolerant controller is designed by using the backstepping method. In the closed‐loop systems, all signals are bounded which is proved through Lyapunov stability theory. Finally, two different road surface disturbances are considered in the simulation, which proves the effectiveness of the proposed method.

Abstract This paper considers a slotless three‐phase tubular permanent magnet generator located in an automotive suspension system for the application of vibration energy harvesting. A two‐dimensional finite element method model of the harvester is produced and an experimental setup that contains the generator is constructed. Signal decomposition methods are applied to measured suspension displacement data and the resulting signal components are used as input for the model. Two approaches for signal decomposition are discussed, namely, the discrete Fourier transform and the continuous wavelet transform. The individual emf responses of the model are reconstructed to a single output, while a sideband prediction algorithm accounts for the non‐linearities in the system. The simulation results are compared with the reference measurements conducted on the setup to determine the accuracy of each of the signal decomposition methods.

Abstract To improve the control accuracy and output torque of magnetic suspend and control gyroscope, a Lorentz magnetic bearing with double second auxiliary air gap is proposed, and its mechanical structure is introduced. Frist, the structure and working principle of the Lorentz magnetic bearing with second air gap is introduced and the magnetic circuit model of the magnetic bearing is established by the equivalent magnetic circuit method. Second, on the basis of the circuit model, the maximum magnetic flux density of the winding area is analysed by finite element method. The fluctuation rate of magnetic flux density in winding area with double air gap scheme is 5.9%, and the maximum magnetic flux intensity at the winding coil can reach 0.462T. Finally, a bearing prototype is made, the theoretical calculation and finite element simulation results are verified by experiments.

Abstract In this study, the behaviour of a full‐size prototype of a platform for tidal energy was analysed. The turbine of this platform is formed by two counter‐rotating rotors aligned with the tidal current, such that one rotor is always under the wake of the upstream rotor. The platform was tested under real working conditions; the tidal current was emulated by towing. The results of these tests were used to develop and parameterize a turbine model, which is presented in this paper. The turbine was modelled in terms of power, torque, and thrust coefficients. This model was used to estimate the power curve of the platform and design control strategies that, in addition to maximizing energy production, contribute to the stability of the platform.

Abstract The output‐feedback event‐triggered H∞ controller is designed in this paper for singular active seat suspension systems. The human body is simply separated into two parts (body torso and head) and is considered with the seat suspension system. The accelerations of each part are considered as part of the system states, which makes the system as a singular system. The seat suspension deflection, relative velocity, the accelerations of the seat frame, body torso, and head are defined as the system outputs, which are all measurable in real‐time. The output‐feedback H∞ controller is designed at first. Then, the event‐triggered scheme is designed for the seat suspension system to reduce data transmission. Linear matrix equalities criteria are presented to get the controller and event‐triggered scheme and ensure the admissibility of the seat suspension system. Simulation results are shown to illustrate the effectiveness of the considered control method.

Abstract Regenerative braking system can recovery energy in various electric vehicles. Considering large computation load of global optimization methods, most researches adopt instantaneous or local algorithms to optimize the recuperation energy, and incline to study straight deceleration processes. However, uncertain drivers' intentions limit the potential exploration of economy improvement, and simple test conditions do not reflect the complexity of actual driving cycles. Herein, an innovative control architecture is designed for intelligent vehicles to overcome these challenges to some extent. Compared with traditional vehicles, driverless ones would eliminate drivers' interferences, and have more freedoms to optimize energy recovery, route tracking and dynamics stability. Specifically, a series regenerative braking system is designed, and then a three‐level control architecture is first proposed to coordinate three performances. In the top layer, some rules with maximum recuperation energy is exploited off‐line for optimising the velocity and control commands on‐line. In the middle layer, local algorithm is used to track the commands and complex routes for optimal energy from a global perspective. In the bottom layer, hydraulic and regenerative toques are allocated. Tests are conducted to demonstrate the effectiveness of the design and control schemes.

Abstract This study investigates an outer rotor bearingless flywheel motor for flywheel batteries in vehicle that exhibits the advantages of high integration and low loss due to outer rotor direct‐drive and bearingless technologies. A hybrid excitation outer rotor bearingless flywheel motor with four‐degrees‐of‐freedom radial suspension and permanent magnet bias is also proposed, further improving the advantages of high integration and low loss. In addition, suspension force density is ameliorated and suspension–torque coupling is reduced through structure design. To verify the aforementioned advantages of hybrid excitation outer rotor bearingless flywheel motor, its working principles are introduced and its electromagnetic characteristics are analyzed using the finite element method. Comparisons with 12/8 ORBFM are also conducted by focusing on suspension loss and suspension force density.

Deep learning (DL) has been widely used to promote the development of intelligent fault diagnosis, bringing significant performance improvement. However, most of the existing methods cannot capture the temporal information and global features of mechanical equipment to collect sufficient fault information, resulting in performance collapse. Meanwhile, due to the complex and harsh operating environment, it is difficult to extract fault features stably and extensively using single-source fault diagnosis methods. Therefore, a novel hierarchical vision transformer (NHVT) and wavelet time-frequency architecture combined with a multi-source information fusion (MSIF) strategy has been suggested in this paper to boost stable performance by extracting and integrating rich features. The goal is to improve the end-to-end fault diagnosis performance of mechanical components. First, multi-source signals are transformed into two-dimensional time and frequency diagrams. Then, a novel hierarchical vision transformer is introduced to improve the nonlinear representation of feature maps to enrich fault features. Next, multi-source information diagrams are fused into the proposed NHVT to produce more comprehensive presentations. Finally, we employed two different multi-source datasets to verify the superiority of the proposed NHVT. Then, NHVT outperformed the state-of-the-art approach (SOTA) on the multi-source dataset of mechanical components, and the experimental results show that it is able to extract useful features from multi-source information.

This paper presents a method for the probabilistic evaluation of the failure rates of mechanical components in a typical power take-off (PTO) system of a horizontal-axis tidal stream turbine (HATT). The method is based on a modification of the method of the influence factors, when base failure rates, relevant influence factors and, subsequently, resulting failure rates are treated as random variables. The prior (i.e., initial) probabilistic distribution of the failure rates of a HATT component is generated using data for similar components from other industries, while taking into account actual characteristics of the component and site-specific operating and environmental conditions of the HATT. A posterior distribution of the failure rate is estimated numerically based on a Bayesian approach as new information about the component performance in an operating HATT becomes available. The posterior distribution is then employed to obtain the updated mean and lower and upper confidence limits of the failure rate. The proposed method is illustrated by applying it to the evaluation of the failure rates of two key components of the PTO system of a typical HATT—main seal and main bearing. In particular, it is shown that uncertainty associated with the method itself has a major influence on the failure rate evaluation. The proposed method is useful for the reliability assessment of both PTO designs of new HATTs and PTO systems of operating HATTs.

The integration of responsive components in architecture offers the potential to enhance the experience of the building by giving expression to fleeting, changeable aspects of the environment. Responsive buildings enable a physical response to changes in the environment through specific building elements; in rare cases these responsive elements become an integral and poetic element of a culturally significant work of architecture. In this paper I examine two types of responsiveness, one which concerns the changing environment and another the activities and needs of the building׳s inhabitants. I look at two examples of buildings that illustrate a potential poetic role for architectural components responding to these two types of change, and propose that architects will need to acquire experience with designing for specific rates, scales and types of change before responsive elements will more frequently appear as a poetic and integral part of the building.