scholarly journals Implementation and Transfer of Predictive Analytics for Smart Maintenance: A Case Study

2020 ◽  
Vol 2 ◽  
Author(s):  
Sebastian von Enzberg ◽  
Athanasios Naskos ◽  
Ifigeneia Metaxa ◽  
Daniel Köchling ◽  
Arno Kühn
Keyword(s):  
Predictive Analytics ◽  
Spare Parts ◽  
Sensor Data ◽  
Industrial Plant ◽  
Industrial Manufacturing ◽  
Model Transfer ◽  
Holistic Integration ◽  
Quantitative Impact ◽  
Maintenance Process

Smart maintenance offers a promising potential to increase efficiency of the maintenance process, leading to a reduction of machine downtime and thus an overall productivity increase in industrial manufacturing. By applying fault detection and prediction algorithms to machine and sensor data, maintenance measures (i.e., planning of human resources, materials and spare parts) can be better planned and thus machine stoppage can be prevented. While many examples of Predictive Maintenance (PdM) have been proven successful and commercial solutions are offered by machine and part manufacturers, wide-spread implementation of Smart Maintenance solutions and processes in industrial production is still not observed. In this work, we present a case study motivated by a typical maintenance activity in an industrial plant. The paper focuses on the crucial aspects of each phase of the PdM implementation and deployment process, toward the holistic integration of the solution within a company. A concept is derived for the model transfer to a different factory. This is illustrated by practical examples from a lighthouse factory within the BOOST 4.0 project. The quantitative impact of the deployed solutions is described. Based on empirical results, best practices are derived in the domain and data understanding, the implementation, integration and model transfer phases.

Factory Planning Based on Environmental Information

2010 ◽  
pp. 271-288
Author(s):  
Christian Grünwald
Keyword(s):  
Environmental Hazards ◽  
Environmental Information ◽  
Industrial Plant ◽  
High Complexity ◽  
Environmental Aspects ◽  
Industrial Manufacturing ◽  
Production Processes ◽  
Points Of View ◽  
Planning Production

The high complexity and a multitude of constraints in today’s industrial manufacturing require a detailed planning of techniques as well as processes even before the building of production plants. Apart from technique- or process-related planning parameters, environmental aspects are separately considered in the task of planning. The approach of an integration of environmental information into a tool for planning production processes pursues the combination of both points of view. As a result, the expected generation of substances causing environmental hazards during production can hereby be uncovered even before an industrial plant is built. In this way actions for protecting employees and environment will early be considered and later high-cost conversions of plants are avoidable. The described concept is related to business practise by the development of a prototype in connection with a case study and shows the potential of the integration of both planning views.

scholarly journals A System-Approach for Recoverable Spare Parts Management Using the Discrete Weibull Distribution

2019 ◽  
Vol 11 (19) ◽  
pp. 5180 ◽  
Author(s):  
Riccardo Patriarca ◽  
Tianya Hu ◽  
Francesco Costantino ◽  
Giulio Di Gravio ◽  
Massimo Tronci
Keyword(s):  
Weibull Distribution ◽  
Simulation Model ◽  
System Approach ◽  
Management Strategies ◽  
Spare Parts ◽  
Industrial Plant ◽  
Spare Parts Management ◽  
Control Metric ◽  
Potential Use

Optimal spare parts management strategies allow sustaining a system’s availability, while ensuring timely and effective maintenance. Following a systemic perspective, this paper starts from the Multi-Echelon Technique for Recoverable Item Control (METRIC) to investigate the potential use of a Weibull distribution for modelling items’ demand in case of failure. Adapting the analytic formulation of METRIC through a Discrete Weibull distribution, this study originally proposes a METRIC-based model (DW-METRIC) to be used for modelling the stochastic demand in multi-item systems, in order to ensure process sustainability. The DW-METRIC has been tested in a case study related to an industrial plant constituted by 98 items in a passive redundancy configuration. Comparing the results via a simulation model, the outcomes of the study allow defining applicability criteria for the DW-METRIC, in those settings where the DW-METRIC offers more accurate estimations than the traditional METRIC.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

Innovative real-time sensing of flow dynamics in groundwater and sediments to map contaminant spreading

2021 ◽  
Author(s):  
Goedele Verreydt ◽  
Niels Van Putte ◽  
Timothy De Kleyn ◽  
Joris Cool ◽  
Bino Maiheu
Keyword(s):  
Surface Water ◽  
Groundwater Flow ◽  
Real Time ◽  
Flow Direction ◽  
Flow Dynamics ◽  
Sensor Data ◽  
Time Data ◽  
Mass Fluxes ◽  
First Case

<p>Groundwater dynamics play a crucial role in the spreading of a soil and groundwater contamination. However, there is still a big gap in the understanding of the groundwater flow dynamics. Heterogeneities and dynamics are often underestimated and therefore not taken into account. They are of crucial input for successful management and remediation measures. The bulk of the mass of mass often is transported through only a small layer or section within the aquifer and is in cases of seepage into surface water very dependent to rainfall and occurring tidal effects.</p><p> </p><p>This study contains the use of novel real-time iFLUX sensors to map the groundwater flow dynamics over time. The sensors provide real-time data on groundwater flow rate and flow direction. The sensor probes consist of multiple bidirectional flow sensors that are superimposed. The probes can be installed directly in the subsoil, riverbed or monitoring well. The measurement setup is unique as it can perform measurements every second, ideal to map rapid changing flow conditions. The measurement range is between 0,5 and 500 cm per day.</p><p> </p><p>We will present the measurement principles and technical aspects of the sensor, together with two case studies.</p><p> </p><p>The first case study comprises the installation of iFLUX sensors in 4 different monitoring wells in a chlorinated solvent plume to map on the one hand the flow patterns in the plume, and on the other hand the flow dynamics that are influenced by the nearby popular trees. The foreseen remediation concept here is phytoremediation. The sensors were installed for a period of in total 4 weeks. Measurement frequency was 5 minutes. The flow profiles and time series will be presented together with the determined mass fluxes.</p><p> </p><p>A second case study was performed on behalf of the remediation of a canal riverbed. Due to industrial production of tar and carbon black in the past, the soil and groundwater next to the small canal ‘De Lieve’ in Ghent, Belgium, got contaminated with aliphatic and (poly)aromatic hydrocarbons. The groundwater contaminants migrate to the canal, impact the surface water quality and cause an ecological risk. The seepage flow and mass fluxes of contaminants into the surface water were measured with the novel iFLUX streambed sensors, installed directly in the river sediment. A site conceptual model was drawn and dimensioned based on the sensor data. The remediation concept to tackle the inflowing pollution: a hydraulic conductive reactive mat on the riverbed that makes use of the natural draining function of the waterbody, the adsorption capacity of a natural or secondary adsorbent and a future habitat for micro-organisms that biodegrade contaminants. The reactive mats were successfully installed and based on the mass flux calculations a lifespan of at least 10 years is expected for the adsorption material.  </p>

Practice Report “Smart Disaster Management” — Combining Smart City Data and Citizen Participation to Increase Disaster Resilience

i-com ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 177-193
Author(s):  
Daniel Wessel ◽  
Julien Holtz ◽  
Florian König
Keyword(s):  
Citizen Participation ◽  
Smart City ◽  
Development Process ◽  
Smart Cities ◽  
Government Support ◽  
Sensor Data ◽  
Specific Information ◽  
Mutual Support ◽  
The Everyday

Abstract Smart cities have a huge potential to increase the everyday efficiency of cities, but also to increase preparation and resilience in case of natural disasters. Especially for disasters which are somewhat predicable like floods, sensor data can be used to provide citizens with up-to-date, personalized and location-specific information (street or even house level resolution). This information allows citizens to better prepare to avert water damage to their property, reduce the needed government support, and — by connecting citizens locally — improve mutual support among neighbors. But how can a smart city application be designed that is both usable and able to function during disaster conditions? Which smart city information can be used? How can the likelihood of mutual, local support be increased? In this practice report, we present the human-centered development process of an app to use Smart City data to better prepare citizens for floods and improve their mutual support during disasters as a case study to answer these questions.

scholarly journals Additive manufacturing in the spare parts supply chain: hub configuration and technology maturity

2018 ◽  
Vol 24 (7) ◽  
pp. 1178-1192 ◽  
Author(s):  
Siavash H. Khajavi ◽  
Jan Holmström ◽  
Jouni Partanen
Keyword(s):  
Supply Chain ◽  
Additive Manufacturing ◽  
Spare Parts ◽  
Content Type ◽  
Local Manufacturing ◽  
Efficiency And Effectiveness ◽  
Industrial Operations ◽  
Production Configuration ◽  
Part Production

PurposeInnovative startups have begun a trend using laser sintering (LS) technology patents expiration, namely, by introducing LS additive manufacturing (AM) machines that can overcome utilization barriers, such as the costliness of machines and productivity limitation. The recent rise of this trend has led the authors to investigate this new class of machines in novel settings, including hub configuration. There are various supply chain configurations to supply spare parts in industrial operations. This paper aims to explore the promise of a production configuration that combines the benefits of centralized production with the flexibility of local manufacturing without the huge costs related to it.Design/methodology/approachThis study quantitatively examines the feasibility of different AM-enabled spare parts supply chain configurations. Using cost data extracted from a case study, three scenarios per AM machine technology are modeled and compared.FindingsResults suggest that hub production configuration depending on the utilized AM machines can provide economic efficiency and effectiveness to reduce equipment downtime. While previous studies have suggested the need for AM machines with efficiency for single part production for a distributed supply chain, the findings in this research illustrate the positive relationship between multi-part production capability and the feasibility of a hub manufacturing configuration establishment.Originality/valueThis study explores the promise of a production configuration that combines the benefits of centralized production with the flexibility of local manufacturing without the huge costs related to it. Although the existing body of knowledge contains research on production decentralization, research on various levels of decentralization is lacking. Using a real-world case study, this study aims to compare the feasibility of different levels of decentralization for AM-enabled spare parts supply chains.

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