scholarly journals Supporting the Implementation of Predictive Maintenance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Carolin Wagner ◽  
Bernd Hellingrath
Keyword(s):  
Reference Model ◽  
Theoretical Models ◽  
Industrial Applications ◽  
Predictive Maintenance ◽  
Process Models ◽  
Sensor Data ◽  
Maintenance Optimization ◽  
Intention To Use ◽  
High Level ◽  
Process Reference Model

The perception of predictive maintenance as a proactive maintenance strategy to anticipate and reduce severe and costly failures and by thus increasing asset reliability has grown significantly in recent years. Due to the availability of machine sensor data and the intention to use these data in a purposeful way, the introduction of predictive maintenance provides a logical step towards maintenance optimization in industry. Several German industrial surveys highlight the growing interest in the topic by the majority of the addressed companies. Nevertheless, most of these companies are considering predictive maintenance on their future agenda and are currently only at the beginning of its implementation. This is, in many cases, due to missing internal knowledge and systematic guidance for maintenance practitioners. Existing process models and supportive guidance build on theoretical knowledge from experts; however, they often lack the complexity and challenges of industrial applications. In addition, most theoretical models focus on specific aspects of the entire process, target particular application areas, or present a few high-level steps. This paper, therefore, introduces the Process Reference Model for Predictive Maintenance (PReMMa), a comprehensive three-stage hierarchical process reference model for the implementation of predictive maintenance for industrial applications. The process reference model synthesizes existing process models as well as results from interviews with eleven practitioners from both management consultancies and experts from several industrial fields. With regard to four main phases and the predictive maintenance application, results are presented with consideration of the essential steps, their deliverables as well as the involved persons.

scholarly journals Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1685
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul
Keyword(s):  
Neural Networks ◽  
Activity Recognition ◽  
Human Activities ◽  
Recognition Performance ◽  
Confusion Matrix ◽  
Experimental Studies ◽  
Industrial Applications ◽  
Sensor Data ◽  
Complex Activity ◽  
Activity Data

Sensor-based human activity recognition (S-HAR) has become an important and high-impact topic of research within human-centered computing. In the last decade, successful applications of S-HAR have been presented through fruitful academic research and industrial applications, including for healthcare monitoring, smart home controlling, and daily sport tracking. However, the growing requirements of many current applications for recognizing complex human activities (CHA) have begun to attract the attention of the HAR research field when compared with simple human activities (SHA). S-HAR has shown that deep learning (DL), a type of machine learning based on complicated artificial neural networks, has a significant degree of recognition efficiency. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two different types of DL methods that have been successfully applied to the S-HAR challenge in recent years. In this paper, we focused on four RNN-based DL models (LSTMs, BiLSTMs, GRUs, and BiGRUs) that performed complex activity recognition tasks. The efficiency of four hybrid DL models that combine convolutional layers with the efficient RNN-based models was also studied. Experimental studies on the UTwente dataset demonstrated that the suggested hybrid RNN-based models achieved a high level of recognition performance along with a variety of performance indicators, including accuracy, F1-score, and confusion matrix. The experimental results show that the hybrid DL model called CNN-BiGRU outperformed the other DL models with a high accuracy of 98.89% when using only complex activity data. Moreover, the CNN-BiGRU model also achieved the highest recognition performance in other scenarios (99.44% by using only simple activity data and 98.78% with a combination of simple and complex activities).

scholarly journals Multi-Sensor Fusion for Aerial Robots in Industrial GNSS-Denied Environments

2021 ◽  
Vol 11 (9) ◽  
pp. 3921
Author(s):  
Paloma Carrasco ◽  
Francisco Cuesta ◽  
Rafael Caballero ◽  
Francisco J. Perez-Grau ◽  
Antidio Viguria
Keyword(s):  
Sensor Fusion ◽  
Computational Efficiency ◽  
Probabilistic Approach ◽  
Laser Scanner ◽  
Industrial Applications ◽  
Experimental Results ◽  
Added Value ◽  
Aerial Robots ◽  
3D Localization ◽  
High Level

The use of unmanned aerial robots has increased exponentially in recent years, and the relevance of industrial applications in environments with degraded satellite signals is rising. This article presents a solution for the 3D localization of aerial robots in such environments. In order to truly use these versatile platforms for added-value cases in these scenarios, a high level of reliability is required. Hence, the proposed solution is based on a probabilistic approach that makes use of a 3D laser scanner, radio sensors, a previously built map of the environment and input odometry, to obtain pose estimations that are computed onboard the aerial platform. Experimental results show the feasibility of the approach in terms of accuracy, robustness and computational efficiency.

Development and Achievements of SSM Processes for High Performance Components

2016 ◽  
Vol 256 ◽  
pp. 319-327 ◽  
Author(s):  
Mario Rosso ◽  
Ildiko Peter ◽  
Ivano Gattelli
Keyword(s):  
Industrial Development ◽  
High Performance ◽  
Rear Axle ◽  
Research Work ◽  
Industrial Applications ◽  
Alternative Methods ◽  
Front Suspension ◽  
Large Numbers ◽  
Automotive Parts ◽  
High Level

During the last decades under the enthusiastic and competent guidance of Mr Chiarmetta SSM processes attained in Italy at Stampal Spa (Torino) an unquestionable high level of industrial development with the production of large numbers of high performance automotive parts, like variety of suspension support, engine suspension mounts, steering knuckle, front suspension wheel, arm and rear axle. Among the most highlighted findings SSM processes demonstrated their capability to reduce the existing gap between casting and forging, moreover during such a processes there are the opportunity to better control the defect level.Purpose of this paper is to highlight the research work and the SSM industrial production attained and developed by Mr G.L. Chiarmetta, as well as to give an overview concerning some alternative methods for the production of enhanced performance light alloys components for critical industrial applications and to present an analysis of a new rheocasting process suitable for the manufacturing of high performance industrial components.

A service-oriented context-awareness reasoning framework and its implementation

2018 ◽  
Vol 36 (6) ◽  
pp. 1114-1134 ◽  
Author(s):  
Xiufeng Cheng ◽  
Jinqing Yang ◽  
Lixin Xia
Keyword(s):  
Context Awareness ◽  
Service Providers ◽  
User Behavior ◽  
Contextual Information ◽  
Sensor Data ◽  
Context Information ◽  
Content Type ◽  
Low Level ◽  
Service Oriented ◽  
High Level

PurposeThis paper aims to propose an extensible, service-oriented framework for context-aware data acquisition, description, interpretation and reasoning, which facilitates the development of mobile applications that provide a context-awareness service.Design/methodology/approachFirst, the authors propose the context data reasoning framework (CDRFM) for generating service-oriented contextual information. Then they used this framework to composite mobile sensor data into low-level contextual information. Finally, the authors exploited some high-level contextual information that can be inferred from the formatted low-level contextual information using particular inference rules.FindingsThe authors take “user behavior patterns” as an exemplary context information generation schema in their experimental study. The results reveal that the optimization of service can be guided by the implicit, high-level context information inside user behavior logs. They also prove the validity of the authors’ framework.Research limitations/implicationsFurther research will add more variety of sensor data. Furthermore, to validate the effectiveness of our framework, more reasoning rules need to be performed. Therefore, the authors may implement more algorithms in the framework to acquire more comprehensive context information.Practical implicationsCDRFM expands the context-awareness framework of previous research and unifies the procedures of acquiring, describing, modeling, reasoning and discovering implicit context information for mobile service providers.Social implicationsSupport the service-oriented context-awareness function in application design and related development in commercial mobile software industry.Originality/valueExtant researches on context awareness rarely considered the generation contextual information for service providers. The CDRFM can be used to generate valuable contextual information by implementing more reasoning rules.

scholarly journals Data-Centric Knowledge Discovery Strategy for a Safety-Critical Sensor Application

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Nilamadhab Mishra ◽  
Hsien-Tsung Chang ◽  
Chung-Chih Lin
Keyword(s):  
Knowledge Discovery ◽  
Time Frame ◽  
Industrial Applications ◽  
Sensor Data ◽  
Sensors And Actuators ◽  
Safety Critical ◽  
Sensor Applications ◽  
Hard Time ◽  
Collection Data ◽  
Time Critical

In an indoor safety-critical application, sensors and actuators are clustered together to accomplish critical actions within a limited time constraint. The cluster may be controlled by a dedicated programmed autonomous microcontroller device powered with electricity to perform in-network time critical functions, such as data collection, data processing, and knowledge production. In a data-centric sensor network, approximately 3–60% of the sensor data are faulty, and the data collected from the sensor environment are highly unstructured and ambiguous. Therefore, for safety-critical sensor applications, actuators must function intelligently within a hard time frame and have proper knowledge to perform their logical actions. This paper proposes a knowledge discovery strategy and an exploration algorithm for indoor safety-critical industrial applications. The application evidence and discussion validate that the proposed strategy and algorithm can be implemented for knowledge discovery within the operational framework.

scholarly journals Production and characteristics of fish protein hydrolysate from parrotfish (Chlorurus sordidus) head

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8297 ◽  
Author(s):  
Asep A. Prihanto ◽  
Rahmi Nurdiani ◽  
Annas D. Bagus
Keyword(s):  
Antioxidant Activity ◽  
Protein Hydrolysate ◽  
Essential Amino Acids ◽  
Amino Acid Profile ◽  
Industrial Applications ◽  
Control Treatment ◽  
Degree Of Hydrolysis ◽  
Fish Protein ◽  
Fish Protein Hydrolysate ◽  
High Level

Background Fish byproducts are commonly recognized as low-value resources. In order to increase the value, fish byproducts need to be converted into new products with high functionality such as fish protein hydrolysate (FPH). In this study, FPH manufactured from parrotfish (Chlorurus sordidus) heads using different pH, time and sample ratio was investigated. Methods Hydrolysis reactions were conducted under different pHs (5, 7, and 9) and over different durations (12 and 24 h). Control treatment (without pH adjustment (pH 6.4)) and 0 h hydrolsisis duration were applied. Hydrolysates were characterized with respect to proximate composition, amino acid profile, and molecular weight distribution. The antioxidant activity of the hydrolysate was also observed. Results The pH and duration of hydrolysis significantly affected (p < 0.05) the characteristics of FPH. The highest yield of hydrolysate (49.04 ± 0.90%), with a degree of hydrolysis of 30.65 ± 1.82%, was obtained at pH 9 after 24 h incubation. In addition, the FPH had high antioxidant activity (58.20 ± 0.55%), with a high level of essential amino acids. Results suggested that FPH produced using endogenous enzymes represents a promising additive for food and industrial applications.

Toward Agile Interactive Software Development Process Models for Crowd Source Projects

2014 ◽  
pp. 117-131
Author(s):  
Izzat Alsmadi ◽  
Saqib Saeed
Keyword(s):  
Software Development ◽  
Process Models ◽  
Agile Development ◽  
Software Project ◽  
Interactive Software ◽  
Crowd Sourcing ◽  
Software Projects ◽  
The Public ◽  
Development Models ◽  
High Level

Typical traditional software development models are initially designed for company-style software project teams. They also assume a typical software project that has somewhat clear goals, scope, budget, and plan. Even Agile development models that are very flexible in considering previous project parameters assume somewhat stable team and project structures. However, in recent years, the authors have noticed expansion in software projects that are developed in a very illusive flexible team, scope, budget, and plan structures. Examples of such projects are those projects offered in open competition (also called crowd sourcing) structure for software developers to be part of. In typical open competition projects, initial, high level project ideas are submitted to the public through the Internet. The project initiators give their initial requirements, constraints, and conditions for successful products or submissions. Teams can be organized before or through the competition. Submission and evaluation of deliverables from teams are subjected to project initiator evaluation along with evaluation teams organized through the open competition host. This chapter investigates all traditional project characteristics. The authors elaborate on all those elements that should be modified to fit the open competition agile structure. They use several case studies to demonstrate management issues related to managing software projects in open competitions.

Threat Representation Methods for Composite Service Process Models

2013 ◽  
Vol 4 (2) ◽  
pp. 1-18 ◽  
Author(s):  
Per Håkon Meland ◽  
Erlend Andreas Gjære
Keyword(s):  
Business Processes ◽  
Process Models ◽  
Software Systems ◽  
Related Information ◽  
Process Collaboration ◽  
Version 2.0 ◽  
Composite Services ◽  
Technical Specifications ◽  
Business Process Modeling Notation ◽  
High Level

The Business Process Modeling Notation (BPMN) has become a popular standard for expressing high level business processes as well as technical specifications for software systems. However, the specification does not contain native support to express security information, which should not be overlooked in today’s world where every organization is exposed to threats and has assets to protect. Although a substantial amount of work enhancing BPMN 1.x with security related information already exists, the opportunities provided by version 2.0 have not received much attention in the security community so far. This paper gives an overview of security in BPMN and investigates several possibilities of representing threats in BPMN 2.0, in particular for design-time specification and runtime execution of composite services with dynamic behavior. Enriching BPMN with threat information enables a process-centric threat modeling approach that complements risk assessment and attack scenarios. We have included examples showing the use of error events, escalation events and text annotations for process, collaboration, choreography and conversation diagrams.

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