Search

The fault diagnosis of rotating machinery is vital in industry but traditionally depends on manual expertise, requiring substantial resources. To improve diagnostic accuracy, enable effective condition monitoring, and minimize the impact of faults on operations, advanced diagnostic techniques are essential. Hence, we propose an advanced fault diagnosis framework that leverages improved particle swarm optimization (IPSO), variational mode decomposition (VMD), and probabilistic neural networks (PNN) to accurately diagnose faults in rotating machinery using gear and rolling bearing vibration signals. Initially, the vibration signals are decomposed into intrinsic mode functions via VMD, enabling the capture of subtle but critical fault features. To address parameter selection challenges in VMD, we employed IPSO to optimize the VMD parameters, ensuring the optimal decomposition effect. Further, we refined the feature set by applying Laplace fraction optimization and feature dimensionality reduction, isolating sensitive features that serve as input to a PNN-based fault classification model. Experimental results demonstrated that this IPSO-VMD-PNN framework achieves high diagnostic accuracy for various fault types, establishing it as an effective tool for fault identification in rotating machinery. [ABSTRACT FROM AUTHOR]

Glacial diamictite may provide important information on paleoenvironment and average composition of the upper continental crust (UCC). In this study, we report sedimentary facies, petrological and geochemical characteristics of Neoproterozoic diamictite from a profile of the Luoquan Formation on the southern margin of the North China Block (NCB). Upwards the sampling profile, lithostratigraphic strata vary from massive diamictite with poorly sorted carbonate gravels to laminated diamictite with small gravels of terrestrial detrital materials. Along the profile, CaO-MgO-LOI-Sr values decrease with the increase of SiO2-Al2O3-K2O contents. All these petrological and geochemical variations indicate a change from lodgement till deposition in the proximal of ice sheet to ice-rafting deposition in glacial-marine environment with less dolomite to supply their source. Together with previous studies on diamictite from other outcrops on the NCB, the deposition of Luoquan diamictite reflects that the glaciation on the NCB vanished and the ice-rafting effect weakened with glacial transgression process. In addition, significant co-variations of various elements with La and Al2O3 confirm the significant conservation of most analyzed elements during the sedimentary processes to produce diamictite. [ABSTRACT FROM AUTHOR]

Surface defect detection is a complicated task to achieve both specific class and precise location of each defect. Specifically for industrial scenario, realizing efficient and accuracy-satisfactory surface defect automatic detection is still a big challenge. Therefore, a surface defect detection algorithm based on feature-enhanced YOLO (FE-YOLO) for practical industrial applications is proposed in this paper. For the purpose of efficient detection, we lighten YOLO model by combining deep separable convolution and dense join. And an improved feature pyramid network is proposed to enhance the spatial location correlation for multi-scale detection layer for the sake of high accuracy. Then, a new loss function of prediction box regression is established to boost the detection accuracy under the high Intersection over Union (IoU) threshold and accelerate model convergence. To select anchor boxes of different scale feature detection layers, we propose a statistical-based k-means++ algorithm, which can improve the quality of initial anchors and accelerate the convergence of the proposed model. Two industrial surface defect datasets, NEU-DET dataset and DeepPCB dataset, are used to verify the effectiveness of the proposed FE-YOLO algorithm. Experimental results demonstrate that FE-YOLO algorithm is lightened nearly 80% compared with YOLOV4. The detection speed is better than the other state-of-the-art surface defect detection algorithms. The defects detection accuracy respectively reaches 83.9% and 98.9% for the NEU-DET dataset and DeepPCB dataset, which are better than the state-of-the-art defect detection methods. The end-to-end fast and accurate detection for industrial surface defects is realized. [ABSTRACT FROM AUTHOR]

This paper presents a Cooperative Particle Swarm Optimizer with Depth First Search Strategy (DFS-CPSO), which has better seacrch capality than classical Particle Swarm Optimizer (PSO) in solving multimodal optimization problems. In order to improve the quality of information exchange, the Depth First Search (DFS) strategy is hybridized to Cooperative Particle Swarm Optimization(CPSO), which makes information transfer more effectively and generates better quality solution. Specifically, DFS strategy enables different components of solution vector to exchange information separately with PSO and increases the diversity of the population, so that the information of solution components could be preserved by multiple iterations in CPSO. Confirmatory experiments are performed to prove the effectiveness of employing the DFS strategy to CPSO. The comparative results demonstrate superior performance of DFS-CPSO in solving high dimensional multimodal functions than CPSO and other advanced methods. [ABSTRACT FROM AUTHOR]

The Jinghong mafic–ultramafic complex, exposed in the eastern margin of the Lancangjiang tectonic belt, is related to the subduction of the Paleo‐Tethys Ocean. Its petrogenesis plays a key role in constraining the tectonic evolution of the eastern Paleo‐Tethys Ocean in southwestern China. In this study, we present petrological, geochemical and geochronological results of the Jinghong complex rocks, in order to decipher their origin and tectonic significance. The Jinghong mafic–ultramafic complex was composed of peridotite, gabbro, basalt and minor plagiogranite. Whole‐rock geochemical data of the mafic rocks indicate that they have both MORB and IAB affinities and plot in the back‐arc basin basalt (BABB) field in the FeO∗/MgO vs. TiO2 diagram. Combined with their trace element characteristics, it can be concluded that the Jinghong mafic–ultramafic complex represents an ophiolite suite that was formed in a back‐arc ocean basin. Precise LA‐ICP‐MS zircon U‐Pb dating yielded weighted mean 206Pb/238U ages of 298.4 ± 1.7 Ma, 294.3 ± 1.6 Ma, and 292.8 ± 2.0 Ma for gabbroic rocks from this complex, which indicates that the Jinghong ophiolites were formed during the early Permian (298–293 Ma). We propose that during subduction of the main Paleo‐Tethys Ocean, a back‐arc ocean basin was formed at the east of the Lancangjiang tectonic belt. [ABSTRACT FROM AUTHOR]

The Neoarchean basement rocks of the Qixingtai region in the northwest of western Shandong, eastern North China Craton are divided into western and eastern domains, separated by a ductile shear zone. The western domain is mainly composed of early Neoarchean tonalitic and supracrustal rocks, and the eastern domain is composed of late Neoarchean crustally‐derived granite with diverse early Neoarchean enclaves up to several kilometres long of supracrustal and gneissic TTG‐diorite rocks. This study reports an integrated field, whole‐rock geochemistry and zircon U–Pb‐Hf study on the granites and their enclaves in the eastern domain. The supracrustal enclaves are mainly composed of meta‐basaltic and meta‐ultramafic rocks and are the same in lithological association and formation time as the 2,750–2,700 Ma Liuhang "Formation" in the western area. The TTG‐diorite enclaves can be divided into early and late groups. Magmatic zircons from early TTG‐diorite samples have U–Pb ages of 2,731 ± 17 to 2,748 ± 13 Ma and εHf(t) values of +2.7 to +9.5, whereas trondhjemite samples of the late group have U–Pb zircon ages of 2,588 ± 18 to 2,606 ± 12 Ma and εHf(t) values of +0.6 to +6.5. The Hf isotopic signatures on magmatic zircon from the >2,700 Ma diorite‐tonalites indicate that they are predominantly juvenile crustal additions, probably with minor involvement of older crust in their genesis, as given by tDM (Hf) ages ranging back to 2.92 Ga. The eastern granites range from monzogranite to syenogranite in composition and commonly have negative Eu anomalies, indicating residual plagioclase in the source region. Magmatic zircons from two granite samples have U–Pb ages of 2,516 ± 24 and 2,527 ± 18 Ma and εHf(t) values of +1.0 to +7.0. A fine‐grained syenogranite dyke contains inherited early Neoarchean zircons with εHf(t) values of +1.6 to +7.4. The isotopic signatures of the late Neoarchean granites that dominate the eastern domain show that they were derived from partial melting recycling of older continental crust recognized in the region – early Neoarchean rocks formed as a result of juvenile crustal additions. This is a common signature for late Archean crustal evolution throughout much of the North China Craton, and is the hallmark of some other Archean terranes, such as the Dharwar Craton of south India. The geodynamic explanations for the formation globally of vast amounts of crustally‐derived late Neoarchean granite are discussed. [ABSTRACT FROM AUTHOR]

Froth flotation is a complicated process which is difficult to establish its first-principle model. Due to the fluctuations in the grade of raw ore, adaptively adjusting the set-points is extremely important in the flotation process. The inappropriate set-points easily lead to the instability of the process. This paper presents a fuzzy association rule-based set-point adaptive optimization and control strategy for the antimony flotation process without knowing the system model. Firstly, a fuzzy neural network is constructed as a soft-sensor to estimate the feed grade online because of the lack of efficient measurement equipment. Then, fuzzy association rule is used to mine the hidden relationship between the feed grade with reagent dosages and the optimal set-points. Through data mining from the quantitative database, the fuzzy inference system generates the optimal set-points. To implement satisfactory tracking performance, predictive controller is used to compute the control inputs. Because the system dynamics is unknown, long short-term memory network model is established to predict the future behaviors of the process. Finally, simulations and experiments are carried out to demonstrate the effectiveness of the proposed strategy. Compared to the manual manipulation, which is widely used in flotation processes, our control strategy achieves a better control performance, and the concentrate grades are more in line with the process requirement. [ABSTRACT FROM AUTHOR]

Satisfying the process technical requirements while optimizing the control of oxygen and zinc oxide is the main task for optimal operation of the iron removal process. However, due to the complicated mechanism and varied production conditions, the process is difficult to achieve optimal operation with the simple controller. The manual control, as a result, is extensively used in practice. In this paper, we develop an optimal setting and control (OSC) system for the iron removal process to achieve its technical requirements with minimal process consumptions. The OSC system is composed of a neural network soft-sensor, an optimal setting module, an optimal controller, and a fuzzy-logic-based compensator. First, we define the oxygen reaction efficiency (ORE) to measure the difference between the theoretical oxygen amount and its actual amount. Due to the ORE cannot be measured online and it varies with the production conditions, an adaptive weight radial basis function neural network soft-sensor is designed to estimate it. The adaptive weight adjusting method contributes to improve the adaptability of the soft-sensor, and its convergence is discussed. The optimal setting module provides the set-point of outlet ferrous ion concentration for the optimal operation of every reactor. The steady-state optimal control of oxygen and zinc oxide is then established, and the compensator compensates the control inputs utilizing the feedforward and feedback information. Finally, simulations validate that the ORE is important for the optimal control of the process. Furthermore, industrial experiments are presented to verify the effectiveness and potential of the proposed strategy. [ABSTRACT FROM AUTHOR]

In this paper, a novel three-layer coordinating optimization and control strategy involving fuzzy neural networks (FNNs) is proposed for industrial plant-wide control. This strategy includes a coordinator (top layer), local optimizers (middle layer), and local controllers (bottom layer). The coordinator determines and coordinates set-points of production indices for each process in the plant, aiming to achieve plant-wide optimal control. The local optimizers optimize individual unit and deliver the information to the coordinator. The controllers calculate optimal control inputs for the processes according to the set-points. Model predictive control (MPC) is used in this layer. Also used are FNNs with a gradient-based learning algorithm, which are trained to represent the models of the processes that are controlled by MPC. By constructing a Lyapunov function, convergence of the FNNs is established. Experiments are carried out in the largest zinc hydrometallurgy plant in China. The results demonstrate performance of our strategy in tracking the set-points and saving processing costs. Moreover, the strategy achieves a more optimal overall plant operation when compared to the same FNN-based MPC scheme that does not use the coordinator. [ABSTRACT FROM AUTHOR]

An adaptive generalized predictive control (GPC) scheme for an industrial process is designed based on a neuron adaptive splitting and merging radial basis function neural network (NASM-RBFNN) in this paper. The NASM-RBFNN is developed to identify the dynamic behaviors of the industrial process. In order to provide an accurate prediction model for the GPC, a neuron adaptive splitting and merging strategy and a weighted parameters adaptive correction approach are proposed. The neuron adaptive splitting and merging strategy can automatically add or delete the hidden neurons on-line, and the weighted parameters adaptive correction approach can update the weights based on the error of the neural network. The proposed approaches can enable the NASM-RBFNN to be adapted to the time-varying production condition and stochastic disturbance. The stability analysis and convergence of neuron adaptive splitting and merging strategy and weighted parameters adaptive correction approach are given. Finally, the NASM-RBFNN-based GPC (NASM-RBFNN-GPC) is applied to control the iron removal process in the largest zinc hydrometallurgy plant in China. The industrial experiments demonstrate that the NASM-RBFNN-GPC has a satisfactory tracking and control performance. [ABSTRACT FROM AUTHOR]

In the iron removal process, which is composed of four cascaded reactors, outlet ferrous ion concentration (OFIC) is an important technical index for each reactor. The descent gradient of OFIC indicates the reduced degree of ferrous ions in each reactor. Finding the optimal descent gradient of OFIC is tightly close to the effective iron removal and the optimal operation of the process. This paper proposes a coordinated optimization strategy for setting the descent gradient of OFIC. First, an optimal setting module is established to determine the initial set-points of the descent gradient. The oxygen utilization ratio (OUR), an important parameter in this module, cannot be measured online. Therefore, a self-adjusting RBF (SARBF) neural network with an adaptive learning rate is developed to estimate the OUR. The convergence of the SARBF neural network is discussed. Then, a coordinated optimization strategy is proposed to adjust the set-points of the descent gradient when the measured OFICs drift away from their desired set-pints. If the final OFIC does not satisfy the process requirements, a compensation mechanism is developed to provide a compensation for the set-points of the descent gradient. Finally, industrial experiments in the largest zinc hydrometallurgy plant validate the effectiveness of the proposed coordinated optimization strategy. Our strategy improves the qualified ratio of the OFIC and the quality of the goethite precipitate. More profit is created to the iron removal process after our strategy is applied. [ABSTRACT FROM AUTHOR]

As part of the zinc hydrometallurgy plant, the iron removal process is a complex system with four cascaded reactors. Tighter process-index control is difficult to achieve due to the complicated, long, and time-varying removal process. The control performance is also affected by the quality of the ore source and external disturbances. Little research is documented in the literature to address these difficulties and manual control is widely used. An innovative hybrid control strategy is developed to control the iron removal process’ indices within narrow ranges with minimum cost of additive regents, including oxygen and zinc oxide. This strategy is composed of an optimal setting model, a model-based optimal controller, an integrated prediction model, a fuzzy-logic-based feedforward compensator, and a model feedback adjustor. The optimal setting model automatically optimizes the set-points of the process indices under different production conditions. To achieve the process requirements with minimal cost, the model-based optimal controller is designed. The integrated prediction model is established to provide a more accurate on-line prediction of the process indices by integrating the mechanism prediction model and an error compensation model based on the least-square support vector machine. Based on the predicted process indices, the compensator is developed for the optimal controller. The adjustor provides a parameter adjustment mechanism. Four-week-long industrial experiments in the largest zinc hydrometallurgy plant in China show that the control strategy can not only improve the process-indexes control performance, but also save 6.55% oxygen and 4.61% zinc oxide consumptions, which translates to 222 858 m3oxygen and 1236 t zinc oxide per year (a saving of about $570 000). The hybrid control strategy can be extended to cover other similar processes in the zinc hydrometallurgy and other industries. [ABSTRACT FROM AUTHOR]

Using carbon tetrachloride (CCl4) as extraction agent, the activated sludge from Tianjin Jizhuangzi Sewage Treatment Plant as inoculum, the test study on biodegradability of lubricants was carried out. The test flasks containing the mineral medium, the test oil and the inoculum were placed in incubation together with flasks containing poisoned blanks for periods of 0 and 21 days, respectively. Flasks containing the reference materials in place of the test oil were run in parallel. At the end of the incubation period, the contents of the flasks were subjected to sonic vibration, and were acidified and extracted by using CCl4. The extracts were then analysed by infra-red (IR) spectrometer to measure the maximum absorption of the C-H stretch of CH2-CH3 band at wavelength of 2 930 cm−1. The absorption values were used to calculate the residual oil contents of the poisoned and test flasks. Consequently the biodegradability of the test oil was calculated. The test results indicate that the differences in the biodegradability of test materials in different tests are within 5.5%, and consistent with the data described in Coordinating European Council (CEC) L-33-A-93. The biodegradability of lubricants can be evaluated by this method effectively. [ABSTRACT FROM AUTHOR]