Mitochondria are essential in most eukaryotes and are involved in numerous biological functions including ATP production, cofactor biosyntheses, apoptosis, lipid synthesis, and steroid metabolism. ...Work over the past two decades has uncovered the biogenesis of cellular iron-sulfur (Fe S) proteins as the essential and minimal function of mitochondria. This process is catalyzed by the bacteria-derived iron-sulfur cluster assembly (ISC) machinery and has been dissected into three major steps: de novo synthesis of a 2Fe-2S cluster on a scaffold protein; Hsp70 chaperone-mediated trafficking of the cluster and insertion into 2Fe-2S target apoproteins; and catalytic conversion of the 2Fe-2S into a 4Fe-4S cluster and subsequent insertion into recipient apoproteins. ISC components of the first two steps are also required for biogenesis of numerous essential cytosolic and nuclear Fe S proteins, explaining the essentiality of mitochondria. This review summarizes the molecular mechanisms underlying the ISC protein-mediated maturation of mitochondrial Fe S proteins and the importance for human disease.
•An extended and up-to-date review on bearing fault assessment (BFA) is provided.•Detailed information on signal processing and learning approaches is given.•Fault size and damage degradation ...estimation are stated as the main aims in BFA.•VIB, AE, CUR and VOLT are found as the main signals to extract useful information.•Open problems and challenges on this research topic are discussed.
Health condition monitoring of rotating machinery is a crucial task to guarantee reliability in industrial processes. In particular, bearings are mechanical components used in most rotating devices and they represent the main source of faults in such equipments; reason for which research activities on detecting and diagnosing their faults have increased. Fault detection aims at identifying whether the device is or not in a fault condition, and diagnosis is commonly oriented towards identifying the fault mode of the device, after detection. An important step after fault detection and diagnosis is the analysis of the magnitude or the degradation level of the fault, because this represents a support to the decision-making process in condition based-maintenance. However, no extensive works are devoted to analyse this problem, or some works tackle it from the fault diagnosis point of view. In a rough manner, fault severity is associated with the magnitude of the fault. In bearings, fault severity can be related to the physical size of fault or a general degradation of the component. Due to literature regarding the severity assessment of bearing damages is limited, this paper aims at discussing the recent methods and techniques used to achieve the fault severity evaluation in the main components of the rolling bearings, such as inner race, outer race, and ball. The review is mainly focused on data-driven approaches such as signal processing for extracting the proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions. Finally, new challenges are highlighted in order to develop new contributions in this field.
In this article, a new decomposition theory, feature mode decomposition (FMD), is tailored for the feature extraction of machinery fault. The proposed FMD is essentially for the purpose of ...decomposing the different modes by the designed adaptive finite-impulse response (FIR) filters. Benefitting from the superiority of correlated Kurtosis, FMD takes the impulsiveness and periodicity of fault signal into consideration simultaneously. First, a designed FIR filter bank by Hanning window initialization is used to provide a direction for the decomposition. The period estimation and updating process are then used to lock the fault information. Finally, the redundant and mixing modes are removed in the process of mode selection. The superiority of the FMD is demonstrated to adaptively and accurately decompose the fault mode as well as robust to other interferences and noise using simulated and experimental data collected from bearing single and compound fault. Moreover, it has been demonstrated that FMD has superiority in feature extraction of machinery fault compared with the most popular variational mode decomposition.
•SGMD has good decomposition performance in dealing with complex signals.•SGMD, using symplectic geometry similarity transformation, can keep the essential character of the original signals ...unchanged.•The SGMD method is applied to the noisy signal to verify its robustness.•The SGMD performs better on the diagnosis of rotating machinery compound fault.
Various existed time-series decomposition methods, including wavelet transform, ensemble empirical mode decomposition (EEMD), local characteristic-scale decomposition (LCD), singular spectrum analysis (SSA), etc., have some defects for nonlinear system signal analysis. When the signal is more complex, especially noisy signal, the component signal is forced to decompose into several incomplete components by LCD and SSA. In addition, the wavelet transform and EEMD need user-defined parameters, and they are very sensitive to the parameters. Therefore, a new signal decomposition algorithm, symplectic geometry mode decomposition (SGMD), is proposed in this paper to decompose a time series into a set of independent mode components. SGMD uses the symplectic geometry similarity transformation to solve the eigenvalues of the Hamiltonian matrix and reconstruct the single component signals with its corresponding eigenvectors. Meanwhile, SGMD can efficiently reconstruct the existed modes and remove the noise without any user-defined parameters. The essence of this method is that signal decomposition is converted into symplectic geometry transformation problem, and the signal is decomposed into a set of symplectic geometry components (SGCs). The analysis results of simulation signals and experimental signals indicate that the proposed time-series decomposition approach can decompose the analyzed signals accurately and effectively.
•Quality Function Deployment to design the characteristics of the app.•Software implementation with a cross-platform approach.•Web mobile app AMACA available at ...http://www.meccolt.unito.it/amaca/.•Compute the machinery operation cost in different field operations.
Machinery cost is the major cost item in farm businesses in highly mechanized production systems. Moreover, in the last years, high power machines, advanced technologies, higher cost for spare parts and repairing, and fuel consumption contributed to an even more higher increase of the machinery costs. Many engineering and economic methodological approaches have been implemented to calculate machinery use and cost, but they are almost confined in scientific and technical documentations making it difficult for a farmer to apply these approaches for deciding on buying, leasing, or sharing agricultural machinery.
Information and communications technology (ICT) has an increasingly important role on business processes and provides a powerful foundation to address many daily problems. Today users want to be connected to useful information in real time. To that effect, the aim of this work was to develop an easy-to-use mobile application, called “AMACA” (Agricultural Machine App Cost Analysis) for determining the machinery cost in different field operations and making it available via a web mobile application using a cross-platform approach. The customer-driven Quality Function Deployment QFD approach was implemented in order to link the user expectations with the design characteristics of the app. The AMACA app is free, readily available, and does not require any installation on the end user’s device. It is a cross-platform application meaning that it operates on any device through a web interface and is supported by various browsers. The user can make subsequent calculations by varying the input parameters (fuel price, interest rate, field capacity, tractor power, etc.) and compare the results in a sensitivity analysis basis. AMACA app can support the decisions on whether to purchase a new equipment/tractor (strategic level), the use of own machinery or to hire a service, and also to select the economical appropriate cultivation system (tactical level).
metalworking today, the assessment of the degree of wear of the cutting wedge of the tool during its operation is an extremely relevant and in-demand task. Even though a large number of methods for ...monitoring tool life have been developed, there are no unambiguous mathematical dependencies that determine the degree of wear of the cutting wedge according to indirectly measured data. The article proposes a new mathematical apparatus that has not been used before, which makes it possible to adequately interpolate vibrations and temperature in the contact zone into tool wear. The study aim of the study is to develop a method for indirectly estimating the wear rate of the tool, based on a consistent model of intersystem communication describing the force, thermal, and vibration reactions of the cutting process to the shaping movements of the tool. The study consists of experiments on a measuring stand and numerical modeling of the obtained data in Matlab with a comparative evaluation of them with the results of modeling of the mathematical apparatus proposed in the article. The results show that the mathematical model proposed in the article is applicable for an adequate interpretation of experimental data.
PDF
