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This paper presents the idea and experimental tests of a non-stationary stereovision system used for supervision of a loading crane. The system based on two independent gimbals, equipped with cameras, allows simultaneously to measure and track a group of individual spatial points. The four steps calibration procedure is required for each gimbal axis, camera optics, gimbals positions, and loading crane position. Modern camera systems have been widely applied in several fields of the industry allowing for accurate measurements and real-time quality control. The systems based on stereo camera models and appropriate image analysis algorithms can measure a group of spatial points, complex surfaces, small displacements, or vibrations. There are also several vision systems such as laser tracker, camera mounted on fully controlled rotating head (called gimbal), or TOF camera, which can be used to follow and measure real-time object displacements. Unfortunately, they all have significant individual limitations explained in this paper. Accuracy tests of 3D measurements including active tracking of length pattern and crane tip have been performed within a selected working area. Based on reconstructed 3D data points, different types of geometric errors have been presented. The results revealed effects of accuracy degradation on the workspace boundaries and for higher observation angles, as well as 3D data bias suggesting mechanical issues. Several improvements in calibration and gimbal construction might result in far enough accuracy for normal operations of the crane.

The working conditions of loaders alternate between stages of full or empty loads, loading or unloading, and moving forward or backward, which complicates the vehicle's characteristic response. Based on the K-nearest neighbor (KNN) algorithm and a principal component analysis (PCA) method, stages recognition algorithm under the V-type working conditions of a loader was studied. First, the collected transmission signals were noise-reduced and filtered. Second, the PCA was used to reduce the dimensions of the data. Finally, the working condition samples were established from the data obtained, which were later trained and classified using the KNN algorithm. Compared with the neural network algorithm, the accuracy of the optimized KNN algorithm reaches 99.4%, and its running time is 2.1s. The algorithm described in this paper guarantees a high accuracy under recognition of the loader conditions in a short running time. It can be extended to an intelligent identification using big data and artificial intelligence control of the construction machinery.

In this work, a wireless remote-control function and fuzzy control function were added to the mini-loader of a hybrid gas-electricity powertrain. The loader uses a long-range (LoRa) protocol for communication, which is capable of long-distance signal transmissions and has a high level of sensitivity. A frequency-hopping mechanism based on received signal strength, center frequency, and sensitivity information was constructed for the LoRa system, which aids the transmission of information over long distances in a stable manner. This allows the loader to be utilized in remote regions or topographically complex areas. A fuzzy controller was incorporated to alleviate the fuel consumption problems of the hybrid powertrain and provided efficient and stable hybrid power by tuning the torque and rotation speed of the motor. The functionalized mini-loader has achieved data transfer success rates greater than 90% in wireless transmissions within a range of 100 m. The energy consumption of the functionalized powertrain was lowered by 8%.

Data collection and annotation is a time consuming and costly process, yet necessary for machine vision. Automation of construction equipment relies on seeing and detecting different objects in the vehicle’s surroundings. Construction equipment is commonly used to perform frequent repetitive tasks, which are interesting to automate. An example of such a task is the short-loading cycle, where the material is moved from a pile into the tipping body of a dump truck for transport. To complete this task, the wheel loader needs to have the capability to locate the tipping body of the dump truck. The machine vision system also allows the vehicle to detect unforeseen dangers such as other vehicles and more importantly human workers. In this work, we investigate the viability to perform semi-automatic annotation of video data using linear interpolation. The data is collected using scale-models mimicking a wheel-loaders approach towards a dump truck during the short-loading cycle. To measure the viability of this type of solution, the workload is compared to the accuracy of the model, YOLOv3. The results indicate that it is possible to maintain the performance while decreasing the annotation workload by about 95%. This is an interesting result for this application domain, as safety is critical and retaining the vision system performance is more important than decreasing the annotation workload. The fact that the performance seems to retain with a large workload decrease is an encouraging sign.

The forestry industry all over the world is seeing the need for modernization of its machines toward autonomy. In this paper, we focus on a wheel loader that should operate autonomously in the yard of a pulp-and-paper mill, scooping wood chips from a pile of wood and dropping them into a hopper, which is linked to a conveyor that carries them inside the mill. The modelling of the wheel loader is elaborated first, while taking into account that it is composed of two systems: (i) the vehicle and (ii) the arm carrying the bucket. Notice that the former pertains to the category of articulated vehicles that steer using a different mechanism than the conventional Ackermann steering used in car-like vehicles. As for the latter, it is a 2DOF serial manipulator. The navigation is considered then. Finally, simulation results of the kinematics model are shown in Matlab/Simulink first, then dynamics and 3D animation are added using ROS2/Gazebo. Notice that this work is a first step toward the development of the digital twin of the wheel loader. Later, it will be used as the virtual platform for the validation of the autonomous wheel loader.

This paper presents an excavation material classification methodology that uses wavelet analysis and unsupervised learning on acceleration measurements. The technique was validated by using acceleration data that were acquired from three inertial measurement units (IMUs) on an instrumented 1-tonne capacity wheel loader. One IMU was installed on the loader’s boom and two on the bucket. The acceleration signals were logged for 32 manual excavation trials in three excavation materials with different rock size distributions, and the data were processed offline. The continuous wavelet transform was applied to the acceleration signals to extract features from the acceleration signals. The results show that classifying the wavelet feature set using an unsupervised k-means algorithm provides an average material classification accuracy of 81 %, when attempting to simultaneously classify all three materials.

A real-time multibody model of an off-road vehicle can be used to analyze the dynamics of tasks, such as loading and/or transferring material from deformable ground. This analysis requires an accurate description of the mechanics, hydraulic actuators, and the terrain. The objective of this paper is to introduce a novel, real-time capable, deformable terrain/soil model that can interact with the mechanics of a multibody system model and the dynamics of a hydraulics model. To this end, a tractor is modeled by using a semi-recursive multibody formulation based on velocity transformation. The hydraulic actuation of the tractor's front-loader is modeled by using the lumped fluid theory. The tractor loads and transfers sand material from a deformable sand field (the ground), which is modeled by combining mesh-based and particle-based soil representation approaches for the real-time simulation of soil deformation. The work cycle of the tractor model follows a 3D maneuver that is used to load and transfer sand material. During the digging and dumping operations, the static sand field is converted into sand particles and vice versa respectively. For the presented work cycle, the real-time capability of the system is analyzed and determined. Furthermore, the dynamic actuator forces in the hydraulic cylinders are compared with the static actuator forces. The introduced real-time capable tractor simulation model can be utilized in product development and other product processes.

This paper addresses the modeling control and simulation of a power-split hybrid propulsion system for driving a Wheel Loader. The modeling is performed exploiting the Power-Oriented Graphs (POG) graphical modeling technique. The main elements involved in the system are: an ICE (Internal Combustion Engine), two PMSMs (Permanent Magnet Synchronous Motors) controlled using a vectorial control and powered by a DC bus, a planetary gear set, a DC/DC converter, a supercapacitor acting as energy storage device and the transmission system of the vehicle, including all the physical elements from the gearbox to the vehicle wheels. A control strategy aiming at reducing the ICE operating point fluctuations caused by the transmission load and minimizing the ICE specific fuel consumption is proposed. Simulation results with reference to a typical driving cycle for this type of vehicle are finally presented and commented in detail.

Hydrostatic transmission (HST) is a common hydraulic drive train transmission used in track loaders and other high power off-road vehicles. While reliable, traditional HST with fixed-displacement motors has relatively poor efficiencies. In this paper, alternative HST and series hybrid drive train architectures are proposed to replace the traditional HST to allow the implementation of optimal control and optimization methods for better vehicle fuel efficiency. Besides the optimal control methods applied to minimize fuel consumption, the proposed architectures are also optimized for the best configuration in terms of component sizing. Backward-facing (acausal) simulation models were developed to estimate the fuel efficiency of each architecture. Finally, each architecture is compared for optimized performance and fuel saving relative to the traditional HST as baseline.

A novel electrohydraulic energy converter for hybrid off-road machines is presented. The converter is utilized as a power source for hydraulic actuators enabling electrical energy recuperation. The authors have designed, built and tested a device which combines a 7 kW S3 70% electric motor, a planetary gear and a bent-axis hydraulic pump. The integration aims for a power dense small volume device, which can perform the load cycles e.g. of a log stacker. The load cycles are first simulated to define design boundaries for the motor design. The motor uses a moderate amount of oil inside motor chamber as heat transfer medium bringing the motor losses to the converter outer surfaces to be then dissipated in the ambient. The converter is attached to a log loader and real load-cycle performance is investigated. Real load cycles for a log loader are recorded, the converter performance and thermal behaviour are studied. Motor thermal behaviour and losses are studied with no oil or with a moderate amount of oil in the motor. The converter prototype cleared well the requirements in the load cycles tested.

The response characteristics of the vehicle are complex under different working conditions. To facilitate the calculation and analysis of a variety of attributes, based on the kernel principal component analysis method, loaders are used as the research object of this article, data signals of 11 attributes such as throttle signal, speed, pressure, etc. are collected. After denoising and reconstruction of the original signal, the variance contribution rates of principal components 1-4 are 50.99 %, 29.90 %, 14.50 % and 4.27 % by using the kernel principal component analysis. The variance contribution rate of the original 11 attributes is 99.66 %, which could accurately reflect the working condition information. The research methods of multiattribute dimension reduction in this paper could be applied to road construction machinery, mining machinery, petroleum machinery and other engineering fields.

In order to solve the existing problems in the design of the triangular track’s load-bearing wheels, a finite element analysis and topology optimization method based on the analysis of typical working modes is proposed combined with the design of a 50-type loader’s triangular tracks. A finite element analysis model of the load-bearing wheels was established, and the topological optimization of the swing plate was completed on the basis of the force analysis of the load-bearing wheels under typical working conditions. Then the secondary design was carried out in combination with its structural and functional requirements. The results of the finite element analysis prove the effectiveness of the method used.

Executing mechanism is the core system of highspeed loader, its performance directly affects the efficiency of high-speed loader. Based on the logical relationship between the failure of various parts and components and the system of the high-speed loader execution mechanism, this paper draws a fault tree, which visually represents the transmission path of each possible failure. Through FMECA method, the harmfulness of each bottom failure is analyzed qualitatively and quantitatively, and an intelligent maintenance decision system based on inference machine and knowledge base is designed for the execution mechanism of the high-speed loader. The decision-making of diagnosis and maintenance of obstacles provides basis and means.

Remote crane operation has been a subject of research for several years. So far, there is a lack of intuitive human-machine interfaces that resolve present incompatibilities between operator orientation, the crane’s joint movements, and the resulting load movement. This results in poor interface quality and thus in unnecessary time losses, errors, and mental stress. To overcome this gap, an intuitive app interface is developed and applied on a loader crane with boom tip control. An evaluation study with 56 test persons in two experience groups is used to assess the interface’s usability in terms of effectiveness, efficiency, and satisfaction. Results show that the human-machine interface presented here reduces errors and increases the operator’s satisfaction in both user groups. Furthermore, unexperienced crane operators in particular profit from better load placement accuracy and greater time efficiency. The intuitive app-based human-machine interface can therefore improve remote crane operation significantly.

Skid steer loaders are widely used in engineering equipment, when the maximum travel point is reached during the operation of the lift boom. Due to the impact of bucket emergency stop on the connecting parts of hydraulic cylinder and boom, the parts of Skid steer loader boom are damaged. In order to solve this problem, this paper designs the anti impact hydraulic system of skid steer loader boom. The flow control valve and the switch valve are installed in parallel to control the flow rate and reduce the speed, and the simulation model based on AMESim is established. The pressure curve of boom cylinder, the flow curve of flow control valve, the speed curve of bucket and the flow curve of different throttle valve diameter are obtained. The results show that the anti impact function can be realized. The study provides a theoretical basis for the later design and improvement of skid steer loader.

Aiming to be integrated into prosthetic fingertips for the closed-loop sensory feedback, a design of fingertip pressure sensors is realized based on the elastomer and resistance strain gauges. The composite structure of horizontal cross-beam and central vertical column is employed for the elastomer; the deformation of the elastomer is simulated by finite element analysis software ABAQUS; and related parameters including stress and strain are taken into account to ensure the gauges are optimally placed. Calibration and verification experiments are carried out with a force loader and a pinchmeter, whereas pressure signals are demonstrated using a signal acquisition software programmed in Python. Results show the excellent linearity, accuracy, integration and practicality of the designed sensor.

In this paper, we present the design and use of an instrumented glove consisting of a 9-DOF inertial measurement unit (IMU) and resistive flex sensors. The glove is used as a unique human-machine interface to control a Kubota R520s front-end loader, through input gestures, for the excavation of a fragmented rock pile. Raw sensor data from the glove is recorded and transmitted to a computer for gesture recognition. Recognized gestures are then used to command the loader to switch between dig states and control the excavation process. The system allows an operator to observe the entire process from beside the loader, providing them with valuable information about interactions between the loader bucket and rock pile not usually available when seated in the vehicle's cab. Preliminary experiments show that a novice operator was able to improve their performance by using the proposed system, evaluated based on metrics of total and dig completion times, as well as payload.

Bucket-filling is a repetitive task in earth-moving operations with wheel-loaders, which needs to be automated to enable efficient remote control and autonomous operation. Ideally, an automated bucket-filling solution should work for different machine-pile environments, with a minimum of manual retraining. It has been shown that for a given machine-pile environment, a time-delay neural network can efficiently fill the bucket after imitation-based learning from 100 examples by one expert operator. Can such a bucket-filling network be automatically adapted to different machine-pile environments without further imitation learning by optimization of a utility or reward function? This paper investigates the use of a deterministic actor-critic reinforcement learning algorithm for automatic adaptation of a neural network in a new pile environment. The algorithm is used to automatically adapt a bucket-filling network for medium coarse gravel to a cobble-gravel pile environment. The experiments presented are performed with a Volvo L180H wheel-loader in a real-world setting. We found that the bucket-weights in the novel pile environment can improve by five to ten percent within one hour of reinforcement learning with less than 40 bucket-filling trials. This result was obtained after investigating two different reward functions motivated by domain knowledge.

Aiming at the problem that the signal data of mining machinery is often subject to noise interference, this study selects a threshold wavelet denoising method based on the actual working conditions of mining machinery. Taking the front axle signal of the iron ore powder operation by the loader as an example, the threshold wavelet denoising method is used to perform Db9-4 decomposition. After comparison with Butterworth denoising and wavelet transform denoising methods, it is concluded that Noise will cause signal drift. The signal-to-noise ratio of threshold wavelet denoising is 14.32 and the mean square error is 51.91, which is similar to the value of wavelet transform method, but it is more suitable for mining machinery. The research results can also be used in other fields such as construction machinery and agricultural machinery.

Industrial production workshops in all fields are becoming more and more robotized. Flexible workshops have several types of robots, namely mobile robots that move products from one machine to another, flexible cells are loaded and unloaded by robot manipulators. In this work we will discuss the design of a serial robot with six degrees of freedom. Three degrees of freedom to position the effector and three others to guide the wrist. In this context the manipulator arm must load and unload a CNC milling machine ‘TORMACH PCNC 440’ which is installed in our lab. The dimensions of the robot are adapted to the size of the machine, these organs are sized by another research team. In what follows we will discuss the different models allowing us the control of the manipulator. We start with the geometric model, the kinematic model and the static model. The goal is to enslave the arm in position and velocity. Several simulations have been carried out to validate this part of our study. Homogeneous transformation and modified DENAVIT-HARTENBURG (DH) parameters are adopted in this approach.

How to evaluate the module partition results is a key problem to modular design, especially from the viewpoint of product lifecycle. First, this paper proposes an evaluation model consist of different information from two aspects to describe modules and products based on their lifecycle scenario. Second, the formulas are established to express the relationships between factors. Then use the triangle fuzzy functions to transform the qualitative evaluation into quantitative evaluation, so that it is easy to be analyzed. The weight and evaluation value calculating methods are given. At last, a case study is given based upon the working device system of wheel loader, and the result indicates that the model and the calculation method are available and effective.

We present a novel microinjection chip, which combines a pinion-rack cell loader and a 3-dimensional bubble injector probe, in order to increase the efficiency of microinjection and simplify it. The loader features a powerful interaction with magnets, capable of lifting as much as 20 grams with a 400mT strong magnet. Additionally, the bubble injector is easy to use while also being reliable in doing microinjections. Consequently, not only that the loader can work under harsh conditions, it does so while maintaining a high throughput of up to 16 gel beads (as the model of cells) per minutes. Combining this reliable loader with the easily operated injector, this chip will allow anyone with little to no training to perform microinjections.

A hydrostatic transmission enables continuous, bidirectional transfer of torque. Thereby, no friction clutch is required, which makes it ideal for construction machines with high torques and many stop and go movements. However, the controller design is challenging mainly because of the inherent nonlinearities and the hard system constraints. Furthermore, the systems overactuation has to be exploited to avoid operating states with bad efficiencies. In this paper, an optimization-based control concept for real-time application on a commercial electronic control unit (ECU) is presented. Based on a reduced-order mathematical model, an optimisation-based feedforward control and a flatness based feedback control with a nonlinear compensation is derived. The concept is successfully evaluated for a wheel loader on a test track where the desired driving torque is followed and simultaneously the overall efficiency is maximised.

The proposed supply management model is based, firstly, on realizing a Kraljic matrix to obtain a purchasing strategy, and then followed by the homologation of suppliers who are mainly willing to work with an inventory managed by suppliers (VMI) system. The process aims to reduce the delay time in the acquisition of R1600 loader parts so as to meet the customer in 52 days, the frame established by the company under study in reference to the average of 45 days as the sector standard for the same item.

The advanced production systems are composed of separate and distinct systems that operate in both isolation and conjunction, and therefore forms the System-of-Systems (SoS). However, a lot of production systems are classified as safety-critical, for example, due to the interactions between machines and involved materials. From the safety perspective, besides the behaviour of an individual system in SoS, the emergent behaviour of systems that comes from their individual actions and interactions must be considered. An unplanned event or sequence of events in safety-critical production systems may results in human injury or death, damage to machines or the environment. This paper focuses on the construction equipment domain, particularly the quarry site, which solely produce dimension stone and/or gravel products. The principal contribution of this paper is SoS hazard identification and mitigation/elimination for the electric quarry site for which the combination of guide words based collaborative method Hazard and Operability (HAZOP) and Fault Tree Analysis (FTA) are used. The published studies on HAZOP and FTA techniques have not considered the emergent behaviours of different machines. The applicability of particular techniques is demonstrated for individual and emergent behaviours of machines used in the quarry operations, such as autonomous hauler, wheel loader, excavator and crusher.

The machinery sector is going through a paradigm shift towards autonomous operations and connectivity to develop services like predictive maintenance and remote operations. This poses the need for new methods and tools to ensure safety, security and regulatory compliance in addition to reducing lead-time for new products and services. Despite the known benefits of systems engineering and available tool support, their adoption is a challenging task for the machinery industry. This is especially true for traditionally hydraulic industry (THI) such as manufacturers of load handling equipment like loader cranes mounted on trucks. This paper presents experiences introducing new methods and tools at a THI. Specifically, it discusses technical and management challenges introducing systems engineering concepts at a load handling equipment manufacturer. This includes but is not limited to relationship between tool maturity and competence development. In addition, research opportunities related to functional safety identified during international standardization activities are also discussed. For example, the limitations of EN ISO 13849 and EN 62061 (the safety standards for machinery) related to autonomous operations and opportunities for research, and development of tools to support specified processes and their variations are discussed.

The article describes the process of measuring the dynamic loads that occur on the technological equipment of the logger when moving through an obstacle with one board. The order of the experimental research is described. As a result of the field experiment, the magnitudes of the dynamic loads that occur on the logger hull and the process equipment under the influence of disturbing factors were found. Calibration equipment and calibration schedule, oscillograms of experimental studies are given.

A vehicle walking dynamics model considering tire elasticity was established to study the mechanism of vehicle shift quality affected by tire elasticity. The model was applied to shift process analysis and the motion equation of shift process was derived. A simulation model is established by Amesim based on the dynamic model. The shift process of a typical loader was calculated and the calculation results were compared between with and without the consideration of tire elasticity in aspects of vehicle acceleration, instantaneous impact, input and output speed of the gearbox, characteristics of clutch slipping. The results show that during the shift process the tire elasticity makes acceleration changes smoothly; the oncoming clutch has two sliding processes, smaller impact and longer shift time; the impact of oncoming clutch is related to the time it engaged; less total sliding friction work. The conclusion will provide important references for the improvement of shift quality.

In order to increase the energy saving effect of hybrid loader, a power system applied to parallel hybrid loader is proposed. The system structure, working condition characteristics and control strategy of hybrid loader are analysed, and the energy saving effect of working devices is studied. The parameters of the main components of a 5 tons hybrid loader are changed and simulated to find the best when working devices working or not. The simulation results demonstrate that the system installed the influence of working devices on fuel consumption, and whether using working devices causes some differences under dynamic rule-based control strategy. The volume of accumulator are the same while the displacement of pump/motor in hydraulic brake regeneration system using working devices should be bigger than the system under stopping working devices which means the condition of working devices is not negligible in the process of parameter matching for parallel hybrid loader .

This demonstration presents an emulator-based active protection system particularly for IoT malware identification and blocking. The key component of our system is a new design of an application loader and an emulating engine based on Unicorn. We demonstrate using IoT network consisting of IoT gateway and IoT devices where the proposed system can be enabled in face of the infamous Mirai attack. We show that with the aid of emulation engine, malicious commands triggered by Telnet and SSH-based IoT malware can be identified and blocked effectively and efficiently while eliminating the possibility of virtual machine escalation.

In order to fulfill the fixed-height lifting function of the loader working device and reduce the deviation between the memory value of the controller and the actual value, this paper starts with the experimental curve, identifies the root cause of the deviation firstly, then separately analyses the influence of load, speed and target value on the deviation through the experimental data, eliminates the unnecessary factor load on the basis of the control accuracy, and then studies the influence of speed and target value on the deviation, and predicts the deviation by curve fitting and neural network based on the existing data. Finally, the prediction deviation is verified by experiments. The validation results of fixed speed show that the prediction results of neural network deviation meet the accuracy requirement of 94.29% and the prediction results of curve fitting deviation meet the accuracy requirement of 85.71%. These two methods of deviation prediction can basically control the deviation in the range of ±1°. And a further optimization method of threshold control is proposed. The validation results of variable speed show that the method of controlling deviation in advance is still applicable to variable speed. And according to the experimental data of variable speed, an optimization method of reducing speed deviation control is proposed.

This paper presents the development and testing of end-to-end Neural Network (NN) controllers for automated pile loading with a robotic wheel loader. NNs were trained using the Learning from Demonstration approach, i.e. by first recording sensor and control signals during manually-driven pile loading actions. Training made use of three input signals: boom angle, bucket angle and hydrostatic driving pressure; and three output signals: boom control, bucket control and the gas command. Most testing was conducted using NNs with 5 neurons in a single hidden layer, which were able to fill the bucket reasonably well. Qualitative comparisons were made to ascertain how the amount of training data and number of hidden neurons affects bucket filling performance, for NNs trained using both the Levenberg-Marquardt and Bayesian Regularization backpropagation algorithms. Different NNs trained with the same data were also compared. An additional pile transfer experiment compared the performance of an NN controller with a heuristic automated controller and manual human control. By estimating the total volume of material transferred using 3D laser scans, human control was found to have the highest performance, though the NN outperformed the heuristic controller. This indicated that end-to-end NN control trained by demonstration could offer improvement over current heuristic methods for automated pile loading.

The load of wheel loader boom is frequently and widely changed during the lifting and falling process. When the loader arm is lifting, the peak power of the hydraulic pump is high, and when the boom is falling the gravitational potential energy of the boom is dissipated by the hydraulic valve port. Then, the dissipated energy is converted into thermal energy, which increases temperature of the hydraulic oil and reduces the energy efficiency of hydraulic system. In order to improve energy efficiency, a potential energy recovery and wheel loader boom dead-weight hydraulic-gas balance system is proposed in this paper. In the system, the three-chamber hydraulic cylinder and accumulator are used to recycle the potential energy by balancing the weight of boom. In this paper, The co-simulation model of original boom hydraulic system of wheel loader was built in SimulationX firstly. After the model had been verified by experiments, the potential energy recovery and loader arm dead-weight hydraulic-gas balance system is stablished, in which the original two-chamber cylinder of boom hydraulic system is replaced by the three-chamber hydraulic cylinder. Operating characteristics and energy consumption are compared during the lifting and falling simulation process of boom system in no-load conditions. The results show that the proposed system has same operating characteristics with the original machine when the initial pressure of accumulator is 6Mpa, the peak power of pump can be reduced by 57.1 %, and the energy consumption can be decreased by about 39.5%.

In order to reduce energy loss of steering system, a high energy efficiency electronic control independent variable speed closed circuit steering principle is proposed. All the hydraulic valves of the original steering system are removed, the required flow of the hydraulic steering cylinder is supplied by the fixed displacement pump, independently and directly. The servo motor speed is set proportional to the steering speed of an independent electric controlled steering wheel. Thus the output flow and the output volume of the pump match with steering speed and the steering angular of the wheel loader, respectively. To verify the feasibility and stability of the steering system during the automatic steering process, firstly, a co-simulation model of proposed steering system is built in the SimulationX software. Then, in order to validate the dynamic characteristics of the steering system, the test prototype of it is established. Finally, energy consumption of new steering systems is analyzed. Based on the results, by using the energy-saving closed circle pump controlled flow matching steering system the throttling loss, the relief loss and the median unloading loss of the steering process are eliminated, the energy consumption is reduced by 61%. A wheel loader can save about 83. 6 L of fuel. If this energy-saving steering system is adopted to all wheel loaders in china, the carbon emissions of 3.3 * 104 T will be reduced per year.

Imaging an operating system onto a server is an extensive and time consuming process, which commonly taking several minutes to boot. For a testbed administrator, loading an image onto a device is one of the slowest yet most relied upon tasks that a testbed must complete prior to starting an experiment. To minimize wait times for disk loading, as well as to streamline the process of image loading, we introduce Sled, a system-loader for ephemeral devices, that uses warm-booting to quickly image devices. Sled is able to provision a new operating system in under a minute, and is 10 times faster than previous methods of image loading.

The traditional industry is shifting towards fourth industrial revolution called “Industry 4.0” that includes automatic fault detection of machinery. The detection of mechanical faults mainly depends upon accurate condition monitoring of the rotating machinery that requires effective predictive maintenance techniques. The modern industry focusing on predictive maintenance techniques so that these faults can be detected automatically. This article proposed vibration-based power spectral density analysis to detect multiple faults in rolling element bearings. The power spectral density plots were computed at 16.7, 25, 40, and 50 Hz operating speed with a bearing loader and without installing a bearing loader on the vibration data taken from SpectraQuest's Machinery Fault Simulator™ through the accelerometers mounted on inboard and outboard bearing housing. The four various types of faults: outer race, inner race, ball, and combined fault have been analyzed separately in this study. The power spectral density plots of damaged rolling element bearings showed high amplitude vibrations with harmonic behavior. This research article proposed that vibration-based power spectral density is an excellent measurement to detect faults in rolling element bearings that endows technical support to Industry 4.0.

The Portable executable file format is the format of executables, object code and DLL’s (Dynamic Link Library) in Microsoft Windows Operating system. It is the standard of how executable files are organized within file system in Microsoft Windows. The Portable executable file format was designed for Windows NT 3.1 that released in 1993. Many of its features are inherited from COFF (Common object file format) used in Unix Operating systems. It consists of header and sections; headers are rules that tell windows loader how the section should be mapped and loaded into memory. The section are nothing but the data or content. Different sections hold different kind of data for example code section contains executable code while bss (Block Started by Symbol) sections contains uninitialized data. Portable executable file could be used in a way to dent the security of computer, therefore great care should be taken while downloading and running Portable executable files. In our work we develop a static malware analysis tool called ‘Portable Executable File Analysis Framework (PEFAF)’ using data mining techniques. A collection of 8 thousand benign and 7 thousand malicious files were used in this work. We extracted 60 features, analyzed them and found that 34 of them are significant for the detection of malware threats. Based on these 34 indicators our tool classifies input file into malicious or non-malicious.

In this work we propose a strategy using Intel SGX processors to guarantee the use of audited applications in insecure environments. A cloud-based toolchain allows auditors to assess if the user's application meets specifications and standards, to generate the final binaries, and to cryptographically sign them. It also generates a manifesto containing information to verify the authenticity of the audited software binaries. A SGX-based binary loader (inserted by the cloud-based toolchain during the applications building process) writes down auditing data that is encrypted and sealed by SGX functions to form reliable proofs that the original audited software is the one running. As a proof-of-concept, a Linux kernel was modified in order to cryptographically measure all processes being executed and send these results to a SGX application. An analysis was carried out to measure the performance of the altered system. On average, a system consistently running the audit increased the execution time of each process by 20 to 30%.

The aim of this paper is to introduce the reader to basic principles of “data loading” protocol along with concepts of Data Loader implementation. The process of firmware update is described as example of usage ARINC 615A Data Loader. ARINC 615A Data Loader allows a user to upload and download data to or from target hardware of an aircraft. ARINC 615A Data Loader provides four operations: FIND, INFORMATION, UPLOAD and DOWNLOAD. All operations are performed by exchanging protocol files over TFTP (Trivial File Transfer Protocol). Data transfer between Data Loader and LRU (Line Replaceable Unit) in the aircraft network are defined by ARINC 665–3 and ARINC 615A-3 standards.

The fault diagnosis in the automated battery production process is not timely and inaccurate, which will bring huge economic losses to the manufacturing of the power battery. Tray loader section is not only a mechanical link but also an important process link for battery scale production, having a strong process beat requirements. Guaranteeing the correctness of the tray loader section can largely guarantee the correctness of the follow-up process of the automated production of power battery. But, the diagnostic efficiency is very low when the fault diagnosis method is applied to the loading section at this stage. Based on the analysis of the characteristics of the tray loader machine, this paper uses the fault tree analysis method to establish the fault tree model of the loading section. According to this model, a fault diagnosis system based on the fault tree is developed to automatically collect, automatic analysis and automatic diagnosis the data of the loading section. The system has the characteristics of fast diagnosis and high diagnostic accuracy, and can proposed effective solution.

The average mining loader is a diesel-hydraulic off- road mobile machine that is expected to routinely operate in enclosed areas. While traditional valve-controlled setups are common, there are other possible hydraulic systems that might grant benefits to such machines in addition to conventional hybridization. One avenue of improvement lies in electrification, which in itself is advantageous to underground mining machinery that would otherwise require extensive ventilation of their ICE exhaust. Electric power allows the application of direct pump control instead of conventional valve control, eliminating throttling losses. This is one possible method to achieve higher efficiencies when compared to conventional systems. This paper investigates the efficiency of a direct-driven hydraulic system for a mining loader, and compares it to a conventional load-sensing system that was previously installed in the same machine. The efficiency of the direct-driven system was determined to be superior in all tested cases, increasing from 21% to 53% at high velocity and from 2% to 22% at low velocity. In addition, the DDH system is capable of energy regeneration, recouping a portion of energy used for lifting thus allowing longer runtimes with a given battery capacity.

In this paper, installation automatic die bonding machine and optimization parameters were studied in die attach process for integrated circuit (IC) packaging. Die attach process is one that is very important IC packaging manufacturing. This process is attaching a die or chip take to leadframe or substrate using die bonding machine. After installation machine, the parameters of equipment effect to performance of die attach process due to properties of die onto leadframe. For installation, the components of equipment were set up such as wafer table, stack loader, work holder, optical system, dispensing, and bond head before optimization parameters of die attach process. In experiment, the parameters of die bonding machine were optimized such as bond force, bond height, and bond time. The results showed that these parameters lead to optimize performance of die attach process that depend on properties of die bonding such as bond line thickness, die tile, and die shear.

Automatic bucket filling is an open problem since three decades. In this paper, we address this problem with supervised machine learning using data collected from manual operation. The range-normalized actuations of lift joystick, tilt joystick and throttle pedal are predicted using information from sensors on the machine and the prediction errors are quantified. We apply linear regression, k-nearest neighbors, neural networks, regression trees and ensemble methods and find that an ensemble of neural networks results in the most accurate predictions. The prediction root-mean-square-error (RMSE) of the lift action exceeds that of the tilt and throttle actions, and we obtain an RMSE below 0.2 for complete bucket fillings after training with as little as 135 bucket filling examples.

Wheel loader is one of the most widely used heavyduty vehicles for transporting building materials in construction site. Improvement of its efficiency is important for sustainable transport and construction operations. This paper proposes a path optimization approach that allows us to plan loader trajectory and corresponding vehicle motions in construction site when the topological relief information is available. Vehicle dynamics is modeled for 3D motions considering the power bal- ance of vehicle propulsion. The path planning problem is then formulated using a framework of constrained optimal control where vehicle dynamics is incorporated as system constraints. In order to solve the problem, a discrete search method is de- veloped based on the principle of dynamic programming (DP), in which the states of the forward and backward movement paths of wheel loader are explored in parallel. A numerical study is then presented to demonstrate the application of the proposed approach for optimizing the loader path using terrain information.

A robotic platform consisting of a skid-steer compact loader is employed as a setup to design and study strategies for reducing material spillage by robotic mining mobile manipulators. The present work describes the design methodology for a controller approach to reduce disturbances on the end-effector induced by the terrain and propagated through the wheels and arm links of the machine. The proposed approach H ∞ control strategy that includes a feedforward action, computed using the pitch rate of the mobile base, and considers the hydraulic arm dynamics, as well as the reaction forces in the contact points of the mobile base. Alternative control schemes based on the classic proportional-derivative (PD) H ∞ strategy without feedforward action, were also implemented and evaluated on simulations and experiments using a semi-autonomous Cat® 262C compact skid-steer loader equipped with inclination and inertial sensors. The best results were obtained using the proposed approach. The control scheme proposed reduces ground disturbances around 89% and 68% when traversing a 4 m long ramp at 12.5% and 25% of maximum vehicle speed, respectively.