68 research outputs found
Essays on empirical industrial organization
No full textThis dissertation studies platform markets in which a durable device interacts with consumable or content complements, asking how intellectual property policy, licensing choices, and product design shape adoption, pricing, welfare, and environmental outcomes. I develop empirically grounded demand–supply frameworks with indirect network effects, combine structural estimation with reduced-form designs, and recover supply primitives. Across industries, the evidence shows that opening platforms via royalty licensing can expand installed bases and raise long-run surplus; reusable product design can meaningfully displace disposable usage and reduce externalities with limited loss in consumer value; and the composition—especially exclusivity and quality—of complements matters at least as much as sheer variety for hardware adoption. The results integrate environmental accounting into Industrial Organization (IO) analysis of platforms and offer guidance for researchers, managers and policymakers on licensing, content strategy, and support for reusable products.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-12-01The student, Youning Chen, accepted the attached license on 2025-11-18 at 21:10.The student, Youning Chen, submitted this Dissertation for approval on 2025-11-18 at 21:23.This Dissertation was approved for publication on 2025-11-19 at 11:25.DSpace SAF Submission Ingestion Package generated from Vireo submission #22890 on 2026-02-19 at 18:45:4
Health assessment and fault diagnosis for centrifugal pumps using Softmax regression
No full textReal-time health monitoring of industrial components and systems that can detect, classify, and predict impending faults is critical to reduce operating and maintenance costs. This paper presents a softmax regression-based prognostic method for on-line health assessment and fault diagnosis. System conditions are evaluated by processing the information gathered from access controllers or sensors mounted at different points in the system, and maintenance is performed only when the failure or malfunction prognosis is indicated. Wavelet packet decomposition and fast Fourier transform techniques are used to extract features from non-stationary vibration signals. Wavelet packet energies and fundamental frequency amplitude are used as features, and principal component analysis is used for feature reduction. Reduced features are input into softmax regression models to assess machine health and identify possible failure modes. The gradient descent method is used to determine the parameters of softmax regression models. The effectiveness and feasibility of the proposed method are illustrated by applying to a real application
Bearing performance degradation assessment and prediction based on EMD and PCA-SOM
No full textBearings are used in a wide variety of rotating machineries. Bearing vibration signals are non-stationary signals. According to the non-stationary characteristics of bearing vibration signals, a bearing performance degradation assessment/prediction and fault diagnosis method based on empirical mode decomposition (EMD) and principal component analysis (PCA)-self organizing map (SOM) is proposed in this paper. First, vibration signals are decomposed into a finite number of intrinsic mode functions, after which the EMD energy feature vector, which is composed of all the IMF energy, is obtained. PCA is then introduced to reduce the dimension of feature vectors. After that, the reduced feature vectors are selected as input vectors of the SOM neural network for performance degradation and fault diagnosis. Finally, the degradation trend of bearing is predicted by Elman neural network. The analysis results from bearings with different fault degrees or degradation trend and fault patterns show that the proposed method can assess and predict the degradation of bearing suitably and achieve a fault recognition rate of over 95 % for various bearing fault patterns
Bearing performance degradation assessment and prediction based on EMD and PCA-SOM
No full textBearings are used in a wide variety of rotating machineries. Bearing vibration signals are non-stationary signals. According to the non-stationary characteristics of bearing vibration signals, a bearing performance degradation assessment/prediction and fault diagnosis method based on empirical mode decomposition (EMD) and principal component analysis (PCA)-self organizing map (SOM) is proposed in this paper. First, vibration signals are decomposed into a finite number of intrinsic mode functions, after which the EMD energy feature vector, which is composed of all the IMF energy, is obtained. PCA is then introduced to reduce the dimension of feature vectors. After that, the reduced feature vectors are selected as input vectors of the SOM neural network for performance degradation and fault diagnosis. Finally, the degradation trend of bearing is predicted by Elman neural network. The analysis results from bearings with different fault degrees or degradation trend and fault patterns show that the proposed method can assess and predict the degradation of bearing suitably and achieve a fault recognition rate of over 95 % for various bearing fault patterns
Bearing performance degradation assessment and prediction based on EMD and PCA-SOM
No full textBearings are used in a wide variety of rotating machineries. Bearing vibration signals are non-stationary signals. According to the non-stationary characteristics of bearing vibration signals, a bearing performance degradation assessment/prediction and fault diagnosis method based on empirical mode decomposition (EMD) and principal component analysis (PCA)-self organizing map (SOM) is proposed in this paper. First, vibration signals are decomposed into a finite number of intrinsic mode functions, after which the EMD energy feature vector, which is composed of all the IMF energy, is obtained. PCA is then introduced to reduce the dimension of feature vectors. After that, the reduced feature vectors are selected as input vectors of the SOM neural network for performance degradation and fault diagnosis. Finally, the degradation trend of bearing is predicted by Elman neural network. The analysis results from bearings with different fault degrees or degradation trend and fault patterns show that the proposed method can assess and predict the degradation of bearing suitably and achieve a fault recognition rate of over 95 % for various bearing fault patterns
Bearing performance degradation assessment and prediction based on EMD and PCA-SOM
No full textBearings are used in a wide variety of rotating machineries. Bearing vibration signals are non-stationary signals. According to the non-stationary characteristics of bearing vibration signals, a bearing performance degradation assessment/prediction and fault diagnosis method based on empirical mode decomposition (EMD) and principal component analysis (PCA)-self organizing map (SOM) is proposed in this paper. First, vibration signals are decomposed into a finite number of intrinsic mode functions, after which the EMD energy feature vector, which is composed of all the IMF energy, is obtained. PCA is then introduced to reduce the dimension of feature vectors. After that, the reduced feature vectors are selected as input vectors of the SOM neural network for performance degradation and fault diagnosis. Finally, the degradation trend of bearing is predicted by Elman neural network. The analysis results from bearings with different fault degrees or degradation trend and fault patterns show that the proposed method can assess and predict the degradation of bearing suitably and achieve a fault recognition rate of over 95 % for various bearing fault patterns
A novel polyamine-type starch/glycidyl methacrylate copolymer for adsorption of Pb(II), Cu(II), Cd(II) and Cr(III) ions from aqueous solutions
No full text
A novel polyamine-type starch/glycidyl methacrylate (GMA) copolymer with a high capacity for the adsorption of heavy metal ions was prepared via graft copolymerization of GMA and corn starch and a subsequent amination reaction between amino group of diethylenetriamine and epoxy group in GMA. The copolymers were characterized by Fourier transform infrared spectrometry, X-ray diffraction and scanning electron microscopy, and adsorption properties on modified starch of Cu(II), Pb(II), Cd(II) and Cr(III) were studied. By analysing the relationship between adsorption capacity and pH, adsorption isotherms and adsorption kinetics, it is proved that the adsorption of the four metal ions is mainly based on the chemical adsorption of coordination. The maximum adsorption capacities of the copolymer were up to 2.33, 1.25, 0.83 and 0.56 mmol g
−1
for Cu(II), Pb(II), Cd(II) and Cr(III), respectively. The adsorption of the four concerned metal ions was hardly affected by common coexisting ions such as Na(I), K(I), Ca(II) and Mg(II), whereas it was slightly decreased when Fe(II) and Zn(II) coexisted in the solution, which illustrates the selective adsorption of Cu(II), Pb(II), Cd(II) and Cr(III) from wastewater. After 10 cycles of adsorption–desorption experiments, there was no significant change in the adsorption capacity, indicating that the polyamine-type starch/GMA copolymer has high adsorption capacity and good reusability.
</jats:p
Health assessment and fault diagnosis for centrifugal pumps using Softmax regression
No full textReal-time health monitoring of industrial components and systems that can detect, classify, and predict impending faults is critical to reduce operating and maintenance costs. This paper presents a softmax regression-based prognostic method for on-line health assessment and fault diagnosis. System conditions are evaluated by processing the information gathered from access controllers or sensors mounted at different points in the system, and maintenance is performed only when the failure or malfunction prognosis is indicated. Wavelet packet decomposition and fast Fourier transform techniques are used to extract features from non-stationary vibration signals. Wavelet packet energies and fundamental frequency amplitude are used as features, and principal component analysis is used for feature reduction. Reduced features are input into softmax regression models to assess machine health and identify possible failure modes. The gradient descent method is used to determine the parameters of softmax regression models. The effectiveness and feasibility of the proposed method are illustrated by applying to a real application
The Impact of Molybdenum Mining on Cd Pollution along Wenyu Stream in Qinling Mountains, Northwest China
No full textMining has brought many environmental problems to the surrounding soil, water, and air, with toxic elements contaminating surface water, threatening ecological balance and human health. This study selected the Wenyu watershed downstream from a large molybdenum mine in the Qinling Mountains as the study area, aiming to explore the impact of molybdenum mining on surface water quality. The content characteristics of Cd, Pb, Cu, Cr and Hg in surface water, sediment, and rock samples were analyzed by field sampling and chemical testing. The results showed only obvious Cd pollution. The pollution status and ecological risk level of surface water and sediment samples in the Wenyu Stream watershed were evaluated using the single pollution index method, geo-accumulation index method, and Hakanson potential ecological risk assessment method. Finally, the sources of Cd pollution and the impact of mining on Cd distribution in the Wenyu Stream were comprehensively discussed. The research results showed that Cd content in the Wenyu Stream was significantly affected by mining activity and the coefficient of variation of Cd content reached 99.44%. Among 22 surface water samples, 21 samples met the Class II water standard, indicating a clean overall water quality of the Wenyu Stream, and only one sample exceeded the Class II water standard with a mild pollution level. All 15 sediment samples were polluted to varying degrees and the most severely polluted sample had reached a moderate to strong pollution level. Most of the samples were at a moderate pollution level. The potential ecological hazard indexes of Cd content were at medium to very strong risk level, indicating that the overall sediment in the main ditch of the Wenyu Stream was under a strong ecological risk level. The main sources of Cd pollution, including acid mine drainage, regional geological background, sediment release, and atmospheric dry and wet deposition, were discussed
- …
