Machine learning based design optimization method for wave generators of harmonic drives considering multiple performance indicators

The health status assessment of industrial equipment is critical to fault prediction and health management technologies. To fully reflect system degradation and accurately predict the remaining useful life (RUL) of a system, the construction of multiple performance indicators is important. Thus, a new state-space model and RUL prediction method is proposed herein based on multiple indicators, considering the three correlation relationships among features and degradation indicators. First, according to the extracted and selected feature signals, various relationships such as the correlations among multiple features, multiple features and indicators, and multiple indicators are analyzed. Then an intuitive multi-indicator state-space model is established considering the three correlations and the identified degradation patterns. On this basis, three failure modes of the system are defined according to the multi-indicator complex correlation conditions, and the corresponding probability density function of the system RUL is calculated. Finally, the PHM2012 Challenge and XJTU-SY datasets are used as examples for experimental verification. Compared with other prediction methods, the proposed method can intuitively reflect the correlation between features and indicators, to solve the problem caused by the hidden random correlation.

PurposeTotal quality management practices have been embraced by many quality-oriented firms around the world in order to improve performance in terms of quality, productivity, customer satisfaction and profitability. The purpose of this paper is to investigate the direct and indirect effects of TQM practices on various performance indicators specifically in the Indian manufacturing context. This paper focuses on developing an integrated model encompassing significant structural relations showing the linkage between TQM practices and multiple performance indicators – quality performance, customer satisfaction level, operating performance, employee performance, innovation performance, society results and financial performance. Apart from analyzing the direct relationship between constructs, the main purpose of this work is also to identify all the possible mediation effects of performance indicators on others using structural equation modeling (SEM).Design/methodology/approachAn in-depth literature review was conducted to identify the key practices for the successful implementation of TQM in an organization as well as to explore TQM-performance effects. As a result, four TQM practices and seven performance indicators were identified. The data were collected from 260 Indian manufacturing organizations. After confirming the reliability and validity using exploratory and confirmatory factor analysis, the proposed hypotheses were tested using SEM.FindingsThrough testing the proposed hypothesized structural model, the direct and indirect effects of TQM practices have been examined. Moreover, this work developed an integrated model showing the interrelationships between TQM practices and performance indicators identified. The findings gave an insight that the effective implementation of TQM practices assists in reaping benefits in the every facet of an organization. By implementing these practices effectively, managers can expect to realize improvement in all these performance areas.Research limitations/implicationsThis study is subject to certain limitations. Even if all variables were found to be reliable, valid and satisfactory non-response bias test results, the remote possibility of bias in the data might not be fully ruled out. There is a probability of occurrence of common method variance and common method bias, since the data for both dependent and independent variables were collected from the same respondents in the organization. Additionally data on performance indicators were based on the respondent’s assessment and awareness only. The mediating relationship between individual TQM practice and performance indicators can be investigated in future studies. Since society results are a necessity in future, the direct and indirect practices focusing toward this can be explored. In addition, there is a research scope to identify the moderating effect of contextual factors such as degree of TQM implementation, scope of operation and type of organization.Practical implicationsThe findings of the research offer some potentially valuable insights into the relevance of TQM practices and its strong linkage on various performance indicators, through which the overall organization performance can be enhanced. By implementing these practices effectively, managers can expect to realize improvement in all these performance areas. Hence, the managers can adopt this approach to assess their organization’s level in the quality path and as a guideline in implementing TQM practices. They can also measure the impacts of TQM practices on multiple performance measures in order to evaluate their TQM initiatives. Especially the deployment of quality culture is a requisite to excel in the every facet of performance. The positive relationship between TQM practices and various performance indicators can motivate the managers to allocate resources in time, effort and capital for TQM implementation in pursuing quality, leading to customer retention and competitiveness. The findings of the study strongly suggest the need for the holistic implementation of TQM practices for the survival of the organization.Originality/valueWhile there is a considerable volume of researches carried out to investigate the linkage between TQM and organization’s performance across the globe, still little is evidenced regarding the mediating effect of performance indicators on others, especially in the Indian manufacturing context. The present paper attempts to extend and add knowledge to this line of research and to bridge the gap and provide sufficient empirical evidence specifically in the Indian scenario. Thereby helps the organization to follow a guideline to improve the overall performance.

An efficient Bayesian inversion method for seepage parameters using a data-driven error model and an ensemble of surrogates considering the interactions between prediction performance indicators

A digital multimeter working in the marine environment suffers from complex environmental stresses of time-varying temperature, relative humidity and salinity. It is used to measure five indicators of Resistance (R), Direct Current Voltage (DCV), Alternating Current Voltage (ACV), Direct Current (DC) and Alternating Current (AC), and there is an interactive relationship between the five indicators due to the complex structure among the components of multimeter.Considering the measurement errors data of five indicators of digital multimeter as the degradation signal, this paper establishes a degradation model to predict the reliability of each single indicator considering the time-varying environmental stresses based on Brownian motion. The typical D-vine copula is utilized to describe the correlations of multiple performance indicators, the parameters of the optimal D-vine model can be estimated by maximum likelihood estimation. In this paper, lifetime of the multimeter working in marine environment with multiple-performance indicators can be predicted accurately. A case study is presented as an application of this method.

Traffic infrastructure safety is a core topic in traffic construction and development. As the impact of global climate change becomes more and more significant, extreme weather brings more and more safety issues to the normal operation of subway systems. Therefore, it is an urgent issue in the construction of subway systems to fully prepare for extreme weather and improve system resilience under external disturbances. The resilience of a complex system generally refers to its ability to adapt to external disturbances and return to a functional state. As one of several key infrastructure systems in large cities, a subway system needs to be highly resilient to cope with various risks, and it needs to recover quickly under uncertain weather conditions and other external damage events. In order to achieve the goal of conducting a real-time resilience assessment of a subway system, this study adopts the Bayesian network and the traditional failure mode and effect analysis (FMEA) method to realize resilience assessment with multiple performance indicators. Combined with the risk matrix method from FMEA, multiple important indicators of a subway system under the influence of extreme weather are obtained. These important indicators are integrated into the resilience assessment of the subway system within a Bayesian method. In this paper, the feasibility and applicability of the proposed method are verified by taking the Changping Line of the Beijing subway under extreme rainfall weather (>10 mm) as a case.

Machine learning-based multi-performance prediction and analysis of Earth-Air Heat Exchanger

The continuously expanding installed capacity of renewable energy has placed higher demands on the power level of grid-connected converters (GCCs). Consequently, the development of GCCs with higher power, lower cost, and higher performance has become a hotspot in international power electronics research. However, classical GCC technical solutions struggle to meet the trade-offs between multiple performance indicators, including reliability, operating efficiency, power density, power quality, response speed, and cost. To address these issues, an emerging technology involves hybridizing Si and WBG devices in one equipment to combine the high current and high cost-effectiveness of Si inverters with the excellent switching performance of WBG inverters. In this context, this paper reviews in detail the inverters based on hybrid devices (HyDs) and hybrid systems (HySs), which are the two mainstream implementation schemes of Si/WBG hybrid inverters. The latest research on these hybrid inverters is systematically summarized, covering basic principles, hardware design methods, control and optimization strategies, and representative laboratory prototypes. Furthermore, key issues and major challenges in the research and application of Si/WBG hybrid technology are highlighted.

In response to large-scale health crises, emergency healthcare infrastructure projects (EHIPs) have been implemented to provide healthcare services rapidly. However, few studies have systematically explored the success of these projects. This study aims to assess the success of EHIPs throughout the whole life cycle using a cloud matter-element model (CMEM). Considering multiple performance indicators that measure the success of EHIPs, a hierarchy model of the project success index is proposed through eight rounds of Delphi surveys. This model comprises 10 key performance indicators (KPIs) that are most appropriate for measuring the success of EHIPs and 20 quantitative metrics aligned with these KPIs across the life cycle. Based on the hierarchy model, the CMEM is applied to evaluate the success of EHIPs, which effectively addresses the fuzziness and randomness inherent in the Delphi process. Two cases were then utilized for verifying the practicality of the proposed project success assessment model. The findings demonstrate that the CMEM-based assessment offers not only significant flexibility but also high accuracy, robustness, and scalability. The CMEM-based assessment seamlessly integrates numerous incompatible indexes and their characteristic values, without being limited by the number of evaluation indexes. Further, sensitivity analyses are conducted to test the robustness of the assessment model and its results. These analyses confirmed that the CMEM is a robust, reliable, and adaptable tool for assessing the success of EHIPs. Theoretically, this study should set an exemplar of aggregating the performance measurement with emergency management and healthcare systems, facilitating an efficient response to major crises. It also provides a novel approach to evaluating multiple-criteria decision-making problems. Practically, this study should enable project management teams to identify and improve weak areas, ensuring continuous success in the EHIP-related domain.

In this study, an integrated system of harvesting, collecting, and transporting willow biomass crop to a storage site was modeled and evaluated using the IBSAL simulation model. A scenario analysis was used to quantify the impacts of five major input parameters on the performance of the integrated system. These parameters include parcel size, field shape, willow yield, distance to the storage site, and type of the collection equipment. Multiple performance indicators were identified to quantify the impacts on the system such as size of the equipment fleet, effective material and field capacity of the harvester, operating costs, and waiting times. The input data were collected from 36 commercial, short-rotation, shrub willow fields in northern New York State. The simulation results indicated that crop yield and type of collection equipment have the highest impact on operating costs and the equipment fleet size. As the size of equipment fleet increases in the system, variability in the system performance tends to increase. Field shape has the least impact on the overall system performance compared to the other four input parameters. The simulation results suggest that a combination of performance indicators need to be considered to evaluate the overall performance of the dynamic and complex system of harvesting, collection and transportation in commercial willow fields. The developed IBSAL model and scenario analysis approach can assist in planning this system based on the characteristics of field, crop, and logistical equipment to reach a high system performance.

Morphing technologies can improve the flight performance and energy efficiency of hypersonic aircraft. However, conventional trajectory optimization methods are difficult to meet the trajectory optimization requirements with multiple constraints and multiple performance indicators, which limits the performance of hypersonic morphing aircraft. In this paper, a hybrid multiobjective evolutionary algorithm based on decomposition (h-MOEA/D) is developed to solve a multiobjective reentry trajectory optimization problem for hypersonic telescopic wing morphing aircraft. According to the different morphing states of the morphing aircraft, an aerodynamic model is first built using Missile Datcom. An optimization model for the reentry trajectory is then established. Next, based on the MOEA/D framework, the h-MOEA/D components are designed or improved. Chebyshev polynomials are used to parameterize the control variables. Normal boundary intersection (NBI) style Tchebycheff approach and niche-guided scheme are applied to guarantee the uniformity and the diversity for the solution set. The angle-based constrained dominance principle (ACDP) is improved to handle complex constraints. Finally, simulation results show the feasibility and competitiveness of the proposed method compared with other advanced algorithms.

Introduction: The attacks on IoT systems are increasing as the devices and communication networks are progressively integrated. If no attacks are found in IoT for a long time, it will affect the availability of services that can result in data leaks and can create a significant impact on the associated costs and quality of services. Therefore, the attacks and security vulnerability in the IoT ecosystem must be detected to provide robust security and defensive mechanisms for real-time applications. Method: This paper proposes an analytical design of an intelligent attack detection framework using multiple machine learning techniques to provide cost-effective and efficient security analysis services in the IoT ecosystem. Result: The performance validation of the proposed framework is carried out by multiple performance indicators. Conclusion: The simulation outcome exhibits the effectiveness of the proposed system in terms of accuracy and F1-score for the detection of various types of attacking scenarios.

The camshaft profile is one of the important parameters of the engine, the reasonableness or otherwise of its design directly influences the engine’s power output, fuel consumption, emissions, noise and reliability. However, most existing cam profile optimization designs are based on the basic software operation, profile parameter selection and tuning lack a scientific theoretical basis. In order to address this issue, this study establishes a single valve kinematic model of the gas delivery mechanism of diesel engines by using the AVL-EXCITE Timing Drive, and performs a model-based multinomial kinematic acceleration cam profile design. Subsequently, an orthogonal design method is employed to obtain the parameter combinations that can satisfy the dynamic requirements. The research investigates the influence of cam profile design parameters (C 4, p, q, r, and s) on valve fullness, maximum jerk, cam contact stress, and valve seating force through a systematic study. Lastly, based on the grey correlation theory, a thorough analysis of the performance indices of cams with different profile parameter combinations is conducted. The optimization problem of the profile parameters with multiple performance indicators is transformed into a single-objective grey relational optimization problem, resulting in the optimal cam profile parameter combination of C4 =0.1, p=14, q=20, r=28 and s=36. The optimal method proposed in the paper is straightforward to implement and is applicable to the optimum design of cam profiles of the engine, which is of great importance for the optimal design of cam fabrication processes.

Performance prediction of an internal-circulation membrane bioreactor based on models comparison and data features analysis

In many optimization problems, the main goal is to improve a single performance index in which a minimum or maximum value of this index fully reflects the quality of the response obtained from a system. However, in some cases, it is impossible to rely solely on a single index, so a multi-objective optimization problem with multiple performance indicators is considered where the values of all of them should be optimized simultaneously. The mentioned process requires a multi-objective optimization algorithm that can deal with the complexity of problems with simultaneous indexes. This paper presents the multi-objective version of a recently proposed metaheuristic algorithm called Crystal Structure Algorithm (CryStAl) which was inspired by the principles underlying the formation crystal structures. For the performance evaluation of this algorithm which is called MOCryStAl, the benchmark problems of the Completions on Evolutionary Computation (CEC) on multi-objective optimization, called CEC-09, are utilized. Some real-world engineering design problems are used to evaluate the efficiency of the proposed approach. The results demonstrate that the proposed methods can provide excellent results in dealing with the considered multi-objective problems.

Performance measures for object detection evaluation