Measurement 

About: Measurement is an academic journal. The journal publishes majorly in the area(s): Fault (power engineering) & Measurement uncertainty. It has an ISSN identifier of 0263-2241. Over the lifetime, 10289 publication(s) have been published receiving 145861 citation(s).

A Manifesto on Psychology as Idiographic Science: Bringing the Person Back Into Scientific Psychology, This Time Forever.

Abstract: Psychology is focused on variation between cases (interindividual variation). Results thus obtained are considered to be generalizable to the understanding and explanation of variation within single cases (intraindividual variation). It is indicated, however, that the direct consequences of the classical ergodic theorems for psychology and psychometrics invalidate this conjectured generalizability: only under very strict conditions-which are hardly obtained in real psychological processes-can a generalization be made from a structure of interindividual variation to the analogous structure of intraindividual variation. Illustrations of the lack of this generalizability are given in the contexts of psychometrics, developmental psychology, and personality theory.

Condition monitoring and fault diagnosis of planetary gearboxes: A review

Abstract: Planetary gearboxes significantly differ from fixed-axis gearboxes and exhibit unique behaviors, which invalidate fault diagnosis methods working well for fixed-axis gearboxes. Much work has been done for condition monitoring and fault diagnosis of fixed-axis gearboxes, while studies on planetary gearboxes are not that many. However, we still notice that a number of publications on condition monitoring and fault diagnosis of planetary gearboxes have appeared in academic journals, conference proceedings and technical reports. This paper aims to review and summarize these publications and provide comprehensive references for researchers interested in this topic. The structures of a planetary gearbox as well as a fixed-axis one are briefly introduced and contrasted. The unique behaviors and fault characteristics of planetary gearboxes are identified and analyzed. Investigations on condition monitoring and fault diagnosis of planetary gearboxes are summarized based on the adopted methodologies. Finally, open problems are discussed and potential research topics are pointed out.

A sparse auto-encoder-based deep neural network approach for induction motor faults classification

Abstract: This paper presents a deep neural network (DNN) approach for induction motor fault diagnosis. The approach utilizes sparse auto-encoder (SAE) to learn features, which belongs to unsupervised feature learning that only requires unlabeled measurement data. With the help of the denoising coding, partial corruption is added into the input of the SAE to improve robustness of feature representation. Features learned from the SAE are then used to train a neural network classifier for identifying induction motor faults. In addition, to prevent overfitting during the training process, a recently developed regularization method called “dropout” which has been proved to be very effective in neural network was employed. An experiment performed on a machine fault simulator indicates that compared with traditional neural network, the SAE-based DNN can achieve superior performance for feature learning and classification in the field of induction motor fault diagnosis.

Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis

Abstract: Traditional artificial methods and intelligence-based methods of classifying and diagnosing various mechanical faults with high accuracy by extracting effective features from vibration data, such as support vector machines and back propagation neural networks, have been widely investigated. However, the problems of extracting features automatically without significantly increasing the demand for machinery expertise and maximizing accuracy without overcomplicating machine structure have to date remained unsolved. Therefore, a novel hierarchical learning rate adaptive deep convolution neural network based on an improved algorithm was proposed in this study, and its use to diagnose bearing faults and determine their severity was investigated. To test the effectiveness of the proposed method, an experiment was conducted with bearing-fault data samples obtained from a test rig. The method achieved a satisfactory performance in terms of both fault-pattern recognition and fault-size evaluation. In addition, comparison revealed that the improved algorithm is well suited to the fault-diagnosis model, and that the proposed method is superior to other existing methods.

Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method

Abstract: This study focuses on optimizing turning parameters based on the Taguchi method to minimize surface roughness (Ra and Rz). Experiments have been conducted using the L9 orthogonal array in a CNC turning machine. Dry turning tests are carried out on hardened AISI 4140 (51 HRC) with coated carbide cutting tools. Each experiment is repeated three times and each test uses a new cutting insert to ensure accurate readings of the surface roughness. The statistical methods of signal to noise ratio (SNR) and the analysis of variance (ANOVA) are applied to investigate effects of cutting speed, feed rate and depth of cut on surface roughness. Results of this study indicate that the feed rate has the most significant effect on Ra and Rz. In addition, the effects of two factor interactions of the feed rate-cutting speed and depth of cut-cutting speed appear to be important. The developed model can be used in the metal machining industries in order to determine the optimum cutting parameters for minimum surface roughness.