Self-adaptive forecast models in predictive maintenance systems

2018 ◽  
Author(s):  
Edwin Lughofer
Keyword(s):  
Predictive Maintenance ◽  
Forecast Models ◽  
Maintenance Systems ◽  
Self Adaptive

scholarly journals Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads

Computers ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 12 ◽  
Author(s):  
Peeranut Chindanonda ◽  
Vladimir Podolskiy ◽  
Michael Gerndt
Keyword(s):  
Data Processing ◽  
Service Level ◽  
Predictive Maintenance ◽  
Smart Manufacturing ◽  
Smart Devices ◽  
Physical Interaction ◽  
Adaptation Mechanism ◽  
Message Queue ◽  
Adaptive Agent ◽  
Self Adaptive

Internet of Things (IoT) covers scenarios of cyber–physical interaction of smart devices with humans and the environment and, such as applications in smart city, smart manufacturing, predictive maintenance, and smart home. Traditional scenarios are quite static in the sense that the amount of supported end nodes, as well as the frequency and volume of observations transmitted, does not change much over time. The paper addresses the challenge of adapting the capacity of the data processing part of IoT pipeline in response to dynamic workloads for centralized IoT scenarios where the quality of user experience matters, e.g., interactivity and media streaming as well as the predictive maintenance for multiple moving vehicles, centralized analytics for wearable devices and smartphones. The self-adaptation mechanism for data processing IoT infrastructure deployed in the cloud is horizontal autoscaling. In this paper we propose augmentations to the computation schemes of data processing component’s desired replicas count from the previous work; these augmentations aim to repurpose original sets of metrics to tackle the task of SLO violations minimization for dynamic workloads instead of minimizing the cost of deployment in terms of instance seconds. The cornerstone proposed augmentation that underpins all the other ones is the adaptation of the desired replicas computation scheme to each scaling direction (scale-in and scale-out) separately. All the proposed augmentations were implemented in the standalone self-adaptive agent acting alongside Kubernetes’ HPA such that limitations of timely acquisition of the monitoring data for scaling are mitigated. Evaluation and comparison with the previous work show improvement in service level achieved, e.g., latency SLO violations were reduced from 2.87% to 1.70% in case of the forecasted message queue length-based replicas count computation used both for scale-in and scale-out, but at the same time higher cost of the scaled data processor deployment is observed.

Using Rule Quality Measures for Rule Base Refinement in Knowledge-Based Predictive Maintenance Systems

2020 ◽  
Vol 51 (2) ◽  
pp. 161-176
Author(s):  
Qiushi Cao ◽  
Cecilia Zanni-Merk ◽  
Ahmed Samet ◽  
François de Bertrand de Beuvron ◽  
Christoph Reich
Keyword(s):  
Quality Measures ◽  
Predictive Maintenance ◽  
Rule Base ◽  
Knowledge Based ◽  
Maintenance Systems

Predictive Maintenance of Cogeneration Engines Through the Analysis of the Circulating Fluids

2002 ◽  
Author(s):  
J. R. Gonza´lez ◽  
J. Velayos ◽  
M. Comamala
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Diesel Engines ◽  
Predictive Maintenance ◽  
Maintenance System ◽  
Maintenance Systems

In this article we present a fluid-based predictive maintenance system based on an expert system which uses fuzzy logic. The programme uses information from the circulating fluids of the machine to provide an evaluation of the maintenance status of the engine. Specifically, the programme is aimed at diesel engines in a half rate cogeneration, and so we will compare our results with other commercial maintenance systems, such as FAMM (Texaco) and ADOC (Repsol), which provide corresponding responses.

Self-adaptive evolving forecast models with incremental PLS space updating for on-line prediction of micro-fluidic chip quality

2018 ◽  
Vol 68 ◽  
pp. 131-151 ◽  
Author(s):  
Edwin Lughofer ◽  
Robert Pollak ◽  
Alexandru-Ciprian Zavoianu ◽  
Mahardhika Pratama ◽  
Pauline Meyer-Heye ◽  
...  
Keyword(s):  
Chip Quality ◽  
Forecast Models ◽  
On Line ◽  
Self Adaptive

scholarly journals Analysis of the Types of Maintenance Systems of Technical Equipment and the Assessment of the Characteristics of These Systems for Selecting the Optimal Variant

2018 ◽  
Vol 23 (2) ◽  
pp. 131-136
Author(s):  
Ioan Virca
Keyword(s):  
Current Trend ◽  
State Institution ◽  
Point Of View ◽  
Predictive Maintenance ◽  
Technical Equipment ◽  
Flow Energy ◽  
Maintenance System ◽  
Private Organization ◽  
Maintenance Systems ◽  
The Right

Abstract The use and maintenance of technical systems throughout their lifecycle aims at achieving goals that allow for high values of productivity and availability characteristics of these systems. In the economic policy of each state or private organization or state institution, there is the right to determine the type of adequate maintenance system within the maintenance strategy adopted. Thus, the types of maintenance systems recognized in the technical domain are analyzed from the point of view of the two previously-mentioned features, productivity and availability, thus making the predictive maintenance system, derived from the preventively-planned one, advantageous for all organizations. It is obvious that the current trend is to spread this predictive maintenance system, according to which the interventions will be carried out before the malfunction occurs, depending on the periodically measured values of global pressure, flow, energy consumption, temperature, current, voltage, vibrations, etc.

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