Significance of the Predictive Maintenance Strategies for SMEs

2014 ◽  
Author(s):  
Lyubka A. Doukovska ◽  
Vassil G. Nikov ◽  
Vladimir V. Monov ◽  
Stefan L. Kojnov ◽  
Mincho Hadjiski
Keyword(s):  
Predictive Maintenance ◽  
Maintenance Strategies

scholarly journals Estimation of fuel cell operating time for predictive maintenance strategies

2010 ◽  
Vol 35 (15) ◽  
pp. 8022-8029 ◽  
Author(s):  
R. Onanena ◽  
L. Oukhellou ◽  
D. Candusso ◽  
A. Same ◽  
D. Hissel ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Operating Time ◽  
Predictive Maintenance ◽  
Maintenance Strategies

scholarly journals Cost Benefit of Implementing Advanced Monitoring and Predictive Maintenance Strategies for Offshore Wind Farms

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4922
Author(s):  
Alan Turnbull ◽  
James Carroll
Keyword(s):  
Wind Farm ◽  
Cost Benefit ◽  
Wind Farms ◽  
Cost Effective ◽  
Predictive Maintenance ◽  
Offshore Wind ◽  
Component Failure ◽  
Maintenance Strategies ◽  
Early Results ◽  
The Impact

Advancements in wind turbine condition monitoring systems over the last decade have made it possible to optimise operational performance and reduce costs associated with component failure and other unplanned maintenance activities. While much research focuses on providing more automated and accurate fault diagnostics and prognostics in relation to predictive maintenance, efforts to quantify the impact of such strategies have to date been comparatively limited. Through time-based simulation of wind farm operation, this paper quantifies the cost benefits associated with predictive and condition-based maintenance strategies, taking into consideration both direct O&M costs and lost production. Predictive and condition-based strategies have been modelled by adjusting known component failure and repair rates associated with a more reactive approach to maintenance. Results indicate that up to 8% of direct O&M costs can be saved through early intervention along with up to 11% reduction in lost production, assuming 25% of major failures of the generator and gearbox can be diagnosed through advanced monitoring and repaired before major replacement is required. Condition-based approaches can offer further savings compared to predictive strategies by utilising more component life before replacement. However, if weighing up the risk between component failure and replacing a component too early, results suggest that it is more cost effective to intervene earlier if heavy lift vessels can be avoided, even if that means additional major repairs are required over the lifetime of the site.

scholarly journals Two Different Maintenance Strategies in the Hospital Environment: Preventive Maintenance for Older Technology Devices and Predictive Maintenance for Newer High-Tech Devices

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Mana Sezdi
Keyword(s):  
Preventive Maintenance ◽  
Predictive Maintenance ◽  
Hospital Environment ◽  
High Tech ◽  
Device Management ◽  
Maintenance Strategies ◽  
Safety Testing ◽  
Maintenance Program ◽  
Device Reliability ◽  
Different Characteristics

A maintenance program generated through the consideration of characteristics and failures of medical equipment is an important component of technology management. However, older technology devices and newer high-tech devices cannot be efficiently managed using the same strategies because of their different characteristics. This study aimed to generate a maintenance program comprising two different strategies to increase the efficiency of device management: preventive maintenance for older technology devices and predictive maintenance for newer high-tech devices. For preventive maintenance development, 589 older technology devices were subjected to performance verification and safety testing (PVST). For predictive maintenance development, the manufacturers’ recommendations were used for 134 high-tech devices. These strategies were evaluated in terms of device reliability. This study recommends the use of two different maintenance strategies for old and new devices at hospitals in developing countries. Thus, older technology devices that applied only corrective maintenance will be included in maintenance like high-tech devices.

scholarly journals Maintenance Strategies for Industrial Multi-Stage Machines: The Study of a Thermoforming Machine

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6809
Author(s):  
Francisco Javier Álvarez García ◽  
David Rodríguez Salgado
Keyword(s):  
Operating Conditions ◽  
Predictive Maintenance ◽  
Maintenance Strategies ◽  
Industrial Manufacturing ◽  
Multi Stage ◽  
Ideal Approach ◽  
Preventive Programming ◽  
Early Decision ◽  
And Control ◽  
New Strategies

The study of reliability, availability and control of industrial manufacturing machines is a constant challenge in the industrial environment. This paper compares the results offered by several maintenance strategies for multi-stage industrial manufacturing machines by analysing a real case of a multi-stage thermoforming machine. Specifically, two strategies based on preventive maintenance, Preventive Programming Maintenance (PPM) and Improve Preventive Programming Maintenance (IPPM) are compared with two new strategies based on predictive maintenance, namely Algorithm Life Optimisation Programming (ALOP) and Digital Behaviour Twin (DBT). The condition of machine components can be assessed with the latter two proposals (ALOP and DBT) using sensors and algorithms, thus providing a warning value for early decision-making before unexpected faults occur. The study shows that the ALOP and DBT models detect unexpected failures early enough, while the PPM and IPPM strategies warn of scheduled component replacement at the end of their life cycle. The ALOP and DBT strategies algorithms can also be valid for managing the maintenance of other multi-stage industrial manufacturing machines. The authors consider that the combination of preventive and predictive maintenance strategies may be an ideal approach because operating conditions affect the mechanical, electrical, electronic and pneumatic components of multi-stage industrial manufacturing machines differently.

scholarly journals Predictive airframe maintenance strategies using model-based prognostics

2018 ◽  
Vol 232 (6) ◽  
pp. 690-709 ◽  
Author(s):  
Yiwei Wang ◽  
Christian Gogu ◽  
Nicolas Binaud ◽  
Christian Bes ◽  
Raphael T Haftka ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Predictive Maintenance ◽  
Aviation Industry ◽  
Damage Growth ◽  
Maintenance Strategies ◽  
Damage Propagation ◽  
Structural Health ◽  
Model Based ◽  
The Future

Aircraft panel maintenance is typically based on scheduled inspections during which the panel damage size is compared to a repair threshold value, set to ensure a desirable reliability for the entire fleet. This policy is very conservative since it does not consider that damage size evolution can be very different on different panels, due to material variability and other factors. With the progress of sensor technology, data acquisition and storage techniques, and data processing algorithms, structural health monitoring systems are increasingly being considered by the aviation industry. Aiming at reducing the conservativeness of the current maintenance approaches, and, thus, at reducing the maintenance cost, we employ a model-based prognostics method developed in a previous work to predict the future damage growth of each aircraft panel. This allows deciding whether a given panel should be repaired considering the prediction of the future evolution of its damage, rather than its current health state. Two predictive maintenance strategies based on the developed prognostic model are proposed in this work and applied to fatigue damage propagation in fuselage panels. The parameters of the damage growth model are assumed to be unknown and the information on damage evolution is provided by noisy structural health monitoring measurements. We propose a numerical case study where the maintenance process of an entire fleet of aircraft is simulated, considering the variability of damage model parameters among the panel population as well as the uncertainty of pressure differential during the damage propagation process. The proposed predictive maintenance strategies are compared to other maintenance strategies using a cost model. The results show that the proposed predictive maintenance strategies significantly reduce the unnecessary repair interventions, and, thus, they lead to major cost savings.

Sign in / Sign up

Export Citation Format

Share Document