Reliability Importance Measures considering Performance and Costs of Mechanical Hydraulic System for Hydraulic Excavators

Although some reliability importance measures and maintenance policies for mechanical products exist in literature, they are rarely investigated with reference to weakest component identification in the design stage and preventive maintenance interval during the life cycle. This paper is mainly study reliability importance measures considering performance and costs (RIMPC) of maintenance and downtime of the mechanical hydraulic system (MHS) for hydraulic excavators (HE) with energy regeneration and recovery system (ERRS) and suggests the scheduled maintenance interval for key components and the system itself based on the reliability R i t . In the research, the required failure data for reliability analysis is collected from maintenance crews and users over three years of a certain type of hydraulic excavators. Minitab is used for probable distribution estimation of the mechanical hydraulic system failure times, and the model is verified to obey Weibull distribution. RIMPC is calculated by multiplying the reliability R i t and weighting factor W i and then compared with other classical importance measures. The purpose of this paper is to identify the weakest component for MHS in the design stage and to make appropriate maintenance strategies which help to maintain a high reliability level for MHS. The proposed method also provides the scientific maintenance suggestion for improving the MHS reliability of the HE reasonably, which is efficient, profitable, and organized.

Mobile Augmented Reality Application for Component Identification

This article discusses the development of augmented reality (AR) based object identification applications. The purpose of this study is to improve the distribution station component identification workbook. Through the implementation of the developed application, it is assumed that it will be easier to understand the functionality of the components of the distributing station. The augmented reality application was developed for the android platform. The result showed the application of Distributing Station-AR has been developed by applying a marker-based tracking method. The application test has successfully displayed the three-dimension components of distributing station using a marker on the component identification workbook of distributing station. Product performance is known to be very good and product validation is also very good, including material validation, media validation, and first user responses. For further development, this application is expected to add animation of the working principle of AR-based components.

EC-SEG: Electronic Component Segmentation for PCB Assurance and Counterfeit Avoidance

PCB Assurance currently relies on manual physical inspection, which is time consuming, expensive and prone to error. In this study, we propose a novel automated segmentation algorithm to detect and isolate PCB components from the boards called EC-Seg. Segmentation and component localization is a vital preprocessing step in component identification, component authentication, as well as in detecting logos and text markings in components. EC-Seg is an efficient method to automate Quality assurance tool-chains and also to aid Bill of Material Extraction in PCBs. Finally, EC-Seg can be used as a Region proposal algorithm for object detection networks to detect and classify microelectronic components, and also to perform sensor fusion with X-Rays to aid in artifact removal in PCB X-Ray tomography. Index Terms—PCB Hardware Assurance, Component Segmentation, Component detection, AutoBoM, Physical Inspection, Visual inspection, Counterfeit detection

Deteksi Golongan Senyawa Ekstrak Kasar Metabolit Ekstrasel Mikroalga Laut Spirulina sp. Sebagai Agen Antioksidan

Microalgae excrete antioxidant compounds as a defense system to protect themselves from the danger of ultraviolet rays. These compounds also can be used as the organic materials of cosmetics or medicines. This study aimed to determine the antioxidant activity of marine microalgal extracellular metabolite extract of Spirulina sp.. Extracellular metabolites were extracted from the residual media filtrate from the harvesting. The qualitative antioxidant test’s results using the thin-layer chromatography technique and 2,2-diphenylpycrilhydrazil (DPPH) reagent showed antioxidant activity. Moreover, the component identification using ninhydrin and Dragendorff reagent in thin layer chromatography test showed alkaloid and peptide compounds. To support the results, the identification using infrared spectrum analysis showed the peaks at 1117 cm-1 (C-N and C-C stretching), 1458 cm-1 (C-H bending of methyl group), 1635 cm-1 (C=O stretching of amide group), and 3454 cm-1 (N-H stretching of amine and amide groups).

Warehouse Management Models Using Artificial Intelligence Technology with Application at Receiving Stage – A Review

This paper reviewed recent literature on inventory management technologies and Artificial Intelligence (AI) applications. The classical Artificial Neural Network (ANN) models and computer vision technology applications for object classification were reviewed in particularly. The challenges of AI technologies in industrial warehouse management, particularly the ANN for solving object classification and counting are discussed. Some researchers reported the use of face recognition, moving vehicle classification and counting, which are easy to recognise objects on the floor or the ground. Other researchers explored the object counting technologies which are used to identify the visible objects on the ground or in images. Although several studies focused on industrial component identification and counting problems, a study on the warehouse receiving stage remains a blank canvas. This paper reviews and analyses current industrial warehouse management developments around AI applications in this field, which may provide a reference for future researchers and end-users for the best modelling approach to this specific problem at the warehouse receiving stage.

An Ontology-Based Method for Semi-Automatic Disassembly of LCD Monitors and Unexpected Product Types

Disassembly is a vital step in any treatment stream of waste electrical and electronic equipment (WEEE), preventing hazardous and toxic chemicals and materials from damaging the ecosystem. However, the large variations and uncertainties in WEEE is a major limitation to the implementation of automation and robotics in this field. Therefore, the advancement of robotic and automation intelligence to be flexible in handling a variety of situations in WEEE disassembly is sought after. This paper presents an ontology-based cognitive method for generating actions for the disassembly of WEEE, with a focus on LCD monitors, handling uncertainties throughout the disassembly process. The system utilizes reasoning about relationships between a typical LCD monitor product, component features, common fastener types, and actions that the system is capable of, to determine 4 key stages of robotic disassembly: component identification, fastener identification, disassembly action generation, and identification of disassembly extent. Further uncertainties in the form of possible failure of action execution is reasoned about to provide new actions, and any unusual scenarios that result in incorrect reasoning outputs are rectified with user-demonstration as a last resort. The proposed method is trialed for the disassembly of LCD monitors and a product unknown to the system, in the form of a DVD-ROM drive.

Component Analysis of Gas Mixture Based on One-Dimensional Convolutional Neural Network

Indoor harmful gases are a considerable threat to the health of residents. In order to improve the accuracy of indoor harmful gas component identification, we propose an indoor toxic gas component analysis method that is based on the combination of bionic olfactory and convolutional neural network. This method uses the convolutional neural network’s ability to extract nonlinear features and identify each component of bionic oflactory respense signal. A comparison with the results of other methods verifies the improvement of recognition rate while with the same level of time cost, which proved the effectiveness of the proposed model. The experimental results showed that the recognition rate of different types and concentrations of harmful gas components reached 90.96% and it solved the problem of mutual interference between gases.