Rotary Machine Research Articles

Method for On-Line Remaining Useful Life and Wear Prediction for Adjustable Journal Bearings Utilizing a Combination of Physics-Based and Data-Driven Models: A Numerical Investigation

RUL (remaining useful life) estimation is one of the main functions of the predictive analytics systems for rotary machines. Data-driven models based on large amounts of multisensory measurements data are usually utilized for this purpose. The use of adjustable bearings, on the one hand, improves a machine’s performance. On the other hand, it requires considering the additional variability in the bearing parameters in order to obtain adequate RUL estimates. The present study proposes a hybrid approach to such prediction models involving the joint use of physics-based models of adjustable bearings and data-driven models for fast on-line prediction of their parameters. The approach provides a rather simple way of considering the variability of the properties caused by the control systems. It has been tested on highly loaded locomotive traction motor axle bearings for consideration and prediction of their wear and RUL. The proposed adjustable design of the bearings includes temperature control, resulting in an increase in their expected service life. The initial study of the system was implemented with a physics-based model using Archard’s law and Reynolds equation and considering load and thermal factors for wear rate calculation. The dataset generated by this model is used to train an ANN for high-speed on-line bearing RUL and wear prediction. The results show good qualitative and quantitative agreement with the statistics of operation of traction motor axle bearings. A number of recommendations for further improving the quality of predicting the parameters of active bearings are also made as a summary of the work.

Time-frequency ridge estimation: An effective tool for gear and bearing fault diagnosis at time-varying speeds

For a rotary machine vibration signal collected under variable speed conditions, its time–frequency representation (TFR) contains abundant oscillatory components with time-varying amplitudes and frequencies. A single component with a sequence of peaks in the TFR is called a ridge. Accurate ridge detection from TFRs can boost rotary machine health condition assessment without rotation speed measurement. Nowadays, cost function ridge estimation and fast path optimization ridge estimation are the most widely utilized techniques. However, the unreasonable kernel function definitions and inappropriate search region selections significantly restrict the performance of instantaneous frequency estimation of target ridges. To address the deficiencies, this paper proposes a novel time–frequency ridge estimation (TFRE) method. The TFRE integrates a new cost kernel function and an adaptive search region detection principle. For the former, it comprehensively considers the trade-off between seeking peaks and ensuring the smoothness of a ridge. The latter varies the search bandwidth in real-time according to instantaneous signal signatures to effectively isolate interferences and neighboring ridges. A unique advantage of the proposed method is that it dispenses with the tuning of parameters. As a consequence, human intervention is minimized. Experimental gear and bearing vibration signals were analyzed to demonstrate the performance of the TFRE. Results indicated that the proposed TFRE is characterized by superior ridge estimation performance compared to the state-of-the-art methods.

Transient Stability Improvement of Large-Scale Photovoltaic Grid Using a Flywheel as a Synchronous Machine

The global climate protection policy aimed at achieving a zero greenhouse gas emissions target has led to the fast incorporation of large-scale photovoltaics into the power network. The conventional AC grid was not modeled to be incorporated with large-scale non-synchronous inverter-based energy resources (IBR). Incorporating inertia-free IBR into the grid leads to technical issues such as the degradation of system strength and inertia, therefore affecting the safety and reliability of the electrical power system. This research introduced a new solution to incorporate a flywheel in the rotor of a synchronous machine to improve the dynamic inertia control during a system disruption and to maintain the constancy of the system. The objective of this work is to enhance large-scale photovoltaic systems in such a way that they can avoid failures during a fault. A model of transient constancy with two synchronous generators and a LSPV is established in PowerWorld modeling software. A line-to-ground and three-phase fault are simulated in a system with up to 50% IBR penetration. The outcomes showed that the power network was able to ride through faults (RTFs) and that the stability of frequency and voltage are enhanced because of a flywheel that improved grid inertia and strength.

Performance Test of Arabica Coffee Bean Rating Tool Using Image Sensor Guided

The diversity of coffee bean standards is one of the perspectives of coffee bean quality that is very important to customers. Based on the physical (deffect system) the coffee bean sorting process is divided into two, namely manual sorting and mechanical sorting. The purpose of this study is to study the performance of vibrating table type and conveyor table type rating machines in optimizing time and costs in the process before storage, processing and market to consumers. This research was carried out at the Tanah Toraja Coffee Center, Gandangbatu District, Sillanan District. Tana toraja, the industrial teaching laboratory of the Agroindustry study program, the organolepotic test lab of the Jember research center and the Sukamandi flavcoyur laboratory in the month until March of May 2022. The research method used is linear regression. The best result for the operation of the vibrating table type sorting machine is at a slope of the 8o compartment with a rotating speed of 1,310 rpm with the amount of capacity produced as much as 245 kg per hour and for a rotary table type machine the best result is at a speed of 40 rpm rubber belt speed with a time speed of 30 seconds distance with a distance of 1,800 mm, obtained results on the output hole I ( diameter 7 mm) of 9.2 kg per hour, in the output hole II (diameter 6 mm) of 9 kg per hour, and in the output hole III (diameter 5 mm) of 8.6 kg / hour.

Rheology and breakdown energy of a shear zone undergoing flash heating in earthquake-like discrete element models

SUMMARYWe study the initiation and growth of a dry granular shear zone subjected to seismic shearing and flash heating from the perspective of a discrete element method. For this purpose, we created a semi-periodic numerical shear test similar to a rotary shear machine in which a 2 mm ×1.5 mm sample composed of micrometric cohesive disks is sheared in between two rigid walls. The strength of cohesive bonds is defined according to an elasto-brittle contact law calibrated to simulate peak and residual strength envelopes derived from rock mechanics tests. The sample is traversed by a pre-existing fracture and subjected to a vertical confining pressure (e.g. 112.5 MPa) and a velocity step function (e.g. 1 m s–1) applied on the top and bottom walls, respectively. Slip along the fracture induces the growth of a shear zone, which thickens by progressive abrasion of damaged material from cohesive blocks. We carried out two parametric studies to determine the rheology and physical properties of the shear zone for slip velocities and confining pressures characteristic of shallow earthquakes and several flash-heating temperatures. According to parametric studies, the mechanical behavior of the shear zone exhibits three distinct phases. The initial phase of rupture initiation is characterized by the propagation of a shear instability generated by the velocity step (phase 1). During this phase, friction and dilatancy curves are approximated by asymmetric peak functions whose amplitude and geometry are controlled primarily by confining pressure. In the intermediate phase of shear-zone growth, the sample displays an initial transient stage that asymptotically approaches steady state at submelting temperatures (phase 2). According to the inertial number, seismic shearing occurs under quasi-static conditions despite high shear rates. Thus, friction and dilatancy observed in all simulations are roughly constant regardless of slip velocity, confining pressure, and gouge zone thickness. In the final phase of shear weakening, the model evolves toward a new steady state at flash-heating temperatures (phase 3). Average friction and dilatancy are represented by sigmoidal decreasing curves that approach steady-state values lower than for phase 2. Predictably, the thermally weakened friction in steady state (μss ∼ 0.1) is close to the strength of frictionless granular samples sheared in quasi-static conditions. We calculate breakdown energies for the gouge and damage zones and the fracture energy at intermediate and high confining pressures. We show that breakdown energy fundamentally differs from fracture energy commonly used in seismology. The breakdown energy of the damage zones shows long-period damped oscillations weakly correlated with shear-stress fluctuations around average decaying values. Our results suggest that dilatancy is the primary energy sink within the damage zones at steady-state values. The breakdown energy components of the gouge zone follow a similar decaying trend as the average fracture energy but over a longer critical distance. Decohesion and dilatancy are the major energy sinks linked to gouge formation at intermediate pressures. In contrast, dilatancy and debonding frictional energies predominate at high confining pressures. Breakdown energy is equivalent to a fraction of fracture energy that nearly triples when doubling the confining pressure.

On the Behaviour of an AC Induction Motor as Sensor for Condition Monitoring of Driven Rotary Machines

This paper presents some advances in condition monitoring for rotary machines (particularly for a lathe headstock gearbox) running idle with a constant speed, based on the behaviour of a driving three-phase AC asynchronous induction motor used as a sensor of the mechanical power via the absorbed electrical power. The majority of the variable phenomena involved in this condition monitoring are periodical (machines having rotary parts) and should be mechanically supplied through a variable electrical power absorbed by a motor with periodical components (having frequencies equal to the rotational frequency of the machine parts). The paper proposes some signal processing and analysis methods for the variable part of the absorbed electrical power (or its constituents: active and instantaneous power, instantaneous current, power factor, etc.) in order to achieve a description of these periodical constituents, each one often described as a sum of sinusoidal components with a fundamental and some harmonics. In testing these methods, the paper confirms the hypothesis that the evolution of the electrical power (instantaneous and active) has a predominantly deterministic character. Two main signal analysis methods were used, with good, comparable results: the fast Fourier transform of short and long signal sequences (for the frequency domain) and the curve fitting estimation (in the time domain). The determination of the amplitude, frequency and phase at origin of time for each of these components helps to describe the condition (normal or abnormal) of the machine parts. Several achievements confirm the viability of this study: a characterization of a flat driving belt condition and a beating power phenomenon generated by two rotary shafts inside the gearbox. For comparison purposes, the same signal analysis methods were applied to describe the evolution of the vibration signal and the instantaneous angular speed signal at the gearbox output spindle. Many similarities in behaviour among certain mechanical parts (including their electrical power, vibration and instantaneous angular speed) were highlighted.

Intelligent Hybrid Scheme for Health Monitoring of Degrading Rotary Machines: An Adaptive Fuzzy c-Means Coupled With 1-D CNN

The development of a health monitoring scheme for complex rotary machines using unlabeled and imbalanced multivariate data is a well-recognized challenging problem. The challenges and complexity further increase when the health of the machine degrades over time. In order to utilize unlabeled and imbalanced multivariate degradation data, this paper develops an intelligent health monitoring scheme for rotary machines based on the proposed adaptive fuzzy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> -means clustering algorithm followed by ensemble learning, mean-variance-based samples generation method, and 1-D convolutional neural network. The adaptive fuzzy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> -means clustering algorithm uses nondominated sorting genetic algorithm II to automatically determine the different health state labels from unlabeled degradation data. The principle of ensemble learning is used to increase the robustness of the obtained health state labels. Further, based on obtained health states, a new solution is presented to construct the true remaining useful life adaptively. The proposed framework presents a novel method for generating samples based on mean-variance to deal with imbalanced health states. Furthermore, an architecture of 1-D convolutional neural networks for multivariate data is designed to predict the health states and remaining useful life of rotary machines. The proposed approach has been validated through aero-engine and XJTU-SY bearing datasets. The health state prediction accuracy is as high as 99.97% and 100% for aero-engine and XJTU-SY datasets, respectively, using the proposed scheme. The results and comparative studies show the effectiveness of the proposed scheme.

The annulling of the sudden appearance of an unbalance in rotary machines by using active magnetic bearings

The application of magnetic bearings has become more frequent during the last 20 years and represents a significant aspect of improvements in the construction of machines with rotary motion. With the advancement of technology, the number of applications in which magnetic bearings have found their application is increasing. In this paper, it is shown how the effect of magnetic forces can annul the negative influence of unbalance, which suddenly appeared in a rotor supported in active magnetic bearings. Such cases may occur in operation due to breakage and rotor parts falling off (e.g., fan blades), which will lead to a sudden change in the mass balance of the rotor system and dislocation of the centre of mass in relation to the geometric centre of the rotor. In the paper, a mathematical model of the dynamic behaviour of a rigid rotor in active magnetic bearings was developed. The model is nonlinear and has five degrees of freedom and can only be solved numerically. The Newmark beta method and the Newton-Raphson method were used to solve the system of nonlinear differential equations. The results of the simulation showed the advantages of using active magnetic bearings for annulling sudden occurrences of unbalance in rotary machines.

Relational Conduction Graph Network for Intelligent Fault Diagnosis of Rotating Machines Under Small Fault Samples

Fault samples obtained in real-world environment are limited, which makes it hard to diagnose faults of rotating machines (RM) accurately by using the existing intelligent diagnosis methods. To solve the issue above, a new relational conduction graph network (RCGN) is proposed in this paper, which is trained on dataset produced in lab environment to identify fault types of the RM operated in real-world environments. First, feature extractor is constructed to mine fault features from input sample. Second, relational graph network is designed to treat each sample pair as a relational node, and then propagate and aggregate the similarities and relations between samples, so as to mine more discriminative relational characteristics from sample pairs. Moreover, a similarity function is introduced to assess whether the consisting samples in relational node are from the same class to determine fault types. Finally, extensive experiments on two datasets produced in real-world environments are used to validate the superior performance of the RCGN method. The results show that the RCGN method can correctly diagnose fault types of several RM operated in real-world environments, even when each fault type of these RM has only one sample. The diagnostic performance has been greatly improved as compared to state-of-the-art methods.

High-Resolution Fast-Rotating Sound Localization Based on Modal Composition Beamforming and Bayesian Inversion

Rotating source beamforming techniques have been effective means of noise localization on rotary machines. In this letter, we derive an alternative expression for modal composition beamforming (MCB) and subsequently consider the equivalent source assumption and cyclostationarity of the constant angular-speed rotating sound source so that a rotating sound source power (RSP) propagation model is derived. By estimating a suitable solution for the RSP model using the subspace variational Bayesian (SVB) technique with sparsity and total variation (TV) priors, the validity of the RSP model was established. According to the simulation results, the proposed RSP-SVB method leads to a significantly higher resolution than the MCB method. It can localize multiple fast-rotating sound sources accurately, rapidly, and effectively in environments with strong background noise interference. Therefore, our proposed RSP-SVB can offer a reliable solution for identifying fast-rotating blade noise.

Multisource Unsupervised Subdomain Adaptation Network for Rotary Machine Fault Diagnosis

Domain adaptation-based methods have been widely developed for fault diagnosis. However, the existing approaches mainly focus on the global distribution alignment of single-to-single domain without considering multiple scenarios, and overlooking the alignment of subdomains, which causes misclassification near the class boundaries. Thus, a multisource unsupervised subdomain adaptation network is proposed in this paper to solve fault diagnosis of rotary machines under multiple and variable working conditions. Using data images as inputs, a domain feature extractor is constructed to extract the domain-invariant features and map each source-target pair into the advanced feature space. Multi-kernel local maximum mean discrepancy is introduced to align subdomains. Moreover, the domain-specific classifiers are applied to diagnose fault category, and diagnosis result is jointly determined by multiple classifiers through weighted decision-making strategy. The experiment results on two sets of rotary machines achieve average accuracy of 98.01% and 96.04%, respectively, which demonstrates the validity of the proposed method.

Классификация ротационных рабочих органов сельскохозяйственных машин и их траектории движения

The rotary machines and implements used for the cultivation of agricultural crops do not fully meet the agrotechnical requirements. To determine the parameters of the working tools, the authors studied the absolute trajectory of their movement and classified rotary working tools of agricultural machines according to the special location of their rotation axis, dividing them into four groups and seven subgroups: A – the first group of rotors with a horizontal-transverse axis of rotation; B – the second group of rotors with a vertical axis of rotation; C – the third group of rotors with a longitudinal axis of rotation; D, D, E and F are the fourth group of rotors with the axis of rotation located in space relative to the coordinate system XYZ at angles α, β, and γ. The article presents kinematic analysis results for rotary working tools of classes G, D, E, W having a complex location of the axis of rotation in the space. The analysis of calculating the coordinates of the motion trajectory of rotary working tools showed that for the group of rotors of class G, D, E, the motion trajectory corresponds to a compressed cycloid, and for a rotor of class G, the trajectory represents a helicoid. The obtained motion trajectories of material points of rotary working tools can be used to evaluate the process of interaction of working tools with the soil and plant material, to determine the geometric parameters of working tools, the rotor speed, and the translational speed of an agricultural machine.

Application of seismic sensors on measurement while drilling for real-time rock property detection

Rock type and geological structures are often characterized with geotechnical and geological data gathered from boreholes that are drilled considerable distances apart. Such characterization may not identify localized variations associated with rock mass. The continual acquisition of rock data while drilling on a local scale has a potential to provide confidence in detecting rock types and interpreting rock properties, and depending on the application, may inform downstream operations.Drill returned parameters, such as, rotary speed, penetration rate, sound, torque, vibration, can provide insights into rock geotechnical properties. In the past, most of the researches in this area are limited to the use of conventional drill parameters such as penetration rate, rotary speed, torque and weight on bit. Noisy signals from drilling operations often pose a major challenge from using these conventional parameters to reliably interpret rock properties. To overcome this issue, in addition to the conventional drill parameters, the feasibility and application of seismic sensors on identifying relative rock properties have been explored in the current research. The interaction between the drill bit and rock mass being drilled generates seismic signals, which vary with different rock conditions and drilling operations. These seismic signals can be interpreted to detect rock types, rock properties, rock-ore interfaces and geological structures on a local scale.Laboratory experiments with a rotary drilling machine on synthesized rock samples with and without interfaces have been conducted. Preliminary laboratory results show clear seismic signatures when drilling through the rocks with various strengths and interfaces. The results obtained using the conventional drill parameters and seismic data have also been compared in this paper.

In situ strength profiles along two adjacent vertical drillholes from digitalization of hydraulic rotary drilling

Drilling speed and associated analyses from factual field data of hydraulic rotary drilling have not been fully utilized. The paper provides the reference and comparison for the utilization of drilling information from two adjacent vertical drillholes that were formed with the same hydraulic rotary drilling machine and bit. The analysis of original factual data is presented to obtain the constant drilling speed during net drilling process. According to the factual data along two adjacent drillholes, the digitalization results respectively include 461 linear zones and 210 linear zones with their constant drilling speeds and associated drilling parameters. The digitalization results can accurately present the spatial distributions and interface boundaries of drilled geomaterials and the results are consistent with the paralleled site loggings. The weighted average drilling speeds from 2.335 m/min to 0.044 m/min represent 13 types of drilled geomaterials from soils to hard rocks. The quantitative relation between drilling speed and strength property is provided. The digitalization results can statistically profile the basic strength quality grades of III to VI from soils to hard rocks. The thickness distributions of four strength quality grades are presented for each individual type of geomaterials along two drillholes. In total, 50.2% of geomaterials from drillhole A are grade IV and 57.4% of geomaterials from drillhole B are grade III. The digitalization results can offer an accurate and cost-effective tool to quantitatively describe the spatial distribution and in situ strength profile of drilled geomaterials in the current drilling projects. • The paper provides the reference and comparison for the utilization of drilling information from two adjacent vertical drillholes that were formed with the same drilling machine and bit. • According to the factual data along two adjacent drillholes, the digitalization results include linear zones and geomaterial-type zones with their constant drilling speeds and associated drilling parameters. • The digitalization results in the paper can offer the accurate and cost-effective tool to quantitatively describe the spatial distribution and in-situ strength profile of drilled geomaterials in the current drilling projects.

Geometric calculation of non-circular gear segments of the planetary mechanism in rotary hydraulic machines

The article discusses the improvement of the methods of geometric design of positive-displacement hydraulic machines with floating satellites. At the first stage, based on standard methods, an “average” round-link planetary mechanism, having the number of teeth as in the designed hydraulic machine, is calculated. At the second stage, the same-type cyclic functions characterizing the paths of the center of the satellite in the coordinates associated with each of the central gearwheels are specified. At the third stage, the coordinate arrays of the satellite in a variety of its positions are calculated. The angular position of the satellite relative to one of the profiled gearwheels (for example, the sun gearwheel) provides equidistance of the centroid of this gearwheel to the path of the center of the satellite. The angular position of the satellite relative to the other profiled wheel is determined by its running around the first wheel. At the final stage of the design, the satellite is constructed in a variety of positions, and the profile of the corresponding non-circular gear rim is obtained as an envelope of the family of satellite curves-profiles.

Study of drying temperature variation and concentration green spinach (Amaranthus Hybridus l) on characteristics of spinach milk powder

This study aims to determine the effect of variations in drying temperature and concentration of green spinach juice (Amaranthus hybridus l) on spinach milk powder using a rotary vacuum machine. This study uses the research design method used is a factorial randomized block design with 2 repetitions. The treatment design carried out in this study consisted of two factors, namely the drying temperature (T) consisting of 3 levels. The rotary vacuum drying method produces the best characteristics with a moisture content of 2.21%, chlorophyll content of 7.65 mg/l, antioxidant activity of 2024.22 ppm. The results of the best physical analysis were 31.50 seconds of dissolution time, 14.95% yield, 80.18% solubility, 11.35% hygroscopicity, 18.11 Cp viscosity, color intensity L* 63.21, color -a* 7, 00, color b* 26.68.

Diversification of Dairy-Based Food Processed Animal Product by Ice Cream Machine (ICM) at Vocational High School of Muhammadiyah Piyungan

The rotary ice cream machine’s experiment technology improves students’ diverse food processing skills at the Department of Culinary, Vocational High School in Piyungan, Bantul. An ice cream machine (ICM) has been granted to facilitate the processing of livestock milk food products. This research provides skill training to students about making good-quality ice cream from goat milk. The ice cream machine is set at 80 rpm to turn for 20 minutes, then put 3 Kg ice block and 100 gr salt mixed into the coolant rim. Thus, the final ice cream product has a food quality standard by melting and texture tests. For the results test, the melting point of plain ice cream plain tasted has a melting time longer than chocolate tasted.

Semi-supervised joint adaptation transfer network with conditional adversarial learning for rotary machine fault diagnosis

At present, artificial intelligence is booming and has made major breakthroughs in fault diagnosis scenarios. However, the high diagnostic accuracy of most mainstream fault diagnosis methods must rely on sufficient data to train the diagnostic models. In addition, there is another assumption that needs to be satisfied: the consistency of training and test data distribution. When these prerequisites are not available, the effectiveness of the diagnosis model declines dramatically. To address this problem, we propose a semi-supervised joint adaptation transfer network with conditional adversarial learning for rotary machine fault diagnosis. To fully utilize the fault features implied in unlabeled data, pseudo-labels are generated through threshold filtering to obtain an initial pre-trained model. Then, a joint domain adaptation transfer network module based on conditional adversarial learning and distance metric is introduced to ensure the consistency of the distribution in two different domains. Lastly, in three groups of experiments with different settings: a single fault with variable load, a single fault with variable speed, and a mixed fault with variable speed and load, it was confirmed that our method can obtain competitive diagnostic performance.

Prehľad doterajšieho výskumu výroby harmonického prevodu

Harmonic Drive, also called Harmonic Drive gear, harmonic gear, or strain wave gearing, mechanical speed-changing device, invented in the 1950s, that reduces the gear ratio of a rotary machine to increase torque. It operates on a principle different from that of conventional speed changers. Harmonic drive products are unique precision speed reducers playing important roles in robots, semiconductor manufacturing systems, factory automation equipment and furthermore, in aerospace equipment that may convey our human dreams.

Vibration analysis of shot wheel in abrasive blasting

Today, abrasive blasting is experiencing rapid development, due to two important factors. The first one is related to the introduction of the throwing wheel as an element that sets the granular media in motion directing them to the treated surface, while the second factor is related to the production of modern machining media. Together, these factors make blasting one of the most efficient mechanical surface preparation methods. Unfortunately, the high erosiveness of the abrasive stream, so appreciated in the process of preparing the treated surface, also contributes to significant wear of the elements in its immediate vicinity. The undertaken issues focus on the possibilities of classifying various states of the throwing wheel of a rotary machine tool based on the analysis of its dynamic properties.