Predictive Asset Analytics: The Future of Maintenance

2021 ◽  
Author(s):  
Hagar Rabia
Keyword(s):  
Cost Reduction ◽  
Predictive Analytics ◽  
Predictive Maintenance ◽  
Fundamental Aspect ◽  
Maintenance Cost ◽  
Warning Sign ◽  
Full Potential ◽  
Actual Performance ◽  
Interval Extension ◽  
Equipment Performance

Abstract Major Overhauls (MOH) of major Rotating Equipment is an essential activity to ensure equipment and overall plant's productivity and reliability requirements are met. This submission summarizes Maintenance cost reduction and MOH extension benefits on an integrally geared centrifugal Instrument Air (IA) compressor through a first of its kind Predictive Maintenance (PdM) solution project in ADNOC. Appropriate planning for Major Overhauls (MOH) in accordance with OEM, company standards and international best practices are crucial steps. Digitalization continues to transform the industry, with enhancements to maintenance practices a fundamental aspect. Centralized Predictive Analytics & Diagnostics (CPAD) project is a first of its kind in ADNOC as it ventures into on one of the largest predictive maintenance projects in the oil & gas industry. CPAD enables Predictive Maintenance (PdM) through Advanced Pattern Recognition (APR) and Machine Learning (ML) technologies to effectively monitor & assess equipment performance and overall healthiness. Equipment performance is continuously assessed through the developed asset management predictive analytics tool. Through this tool, models associated with the equipment were evaluated to detect performance deviation from historical normal operating behavior. Any deviation from the historical norm would be flagged to indicate condition degradation and/or performance drop. Moreover, the software is configured to alert for subtle changes in the system behavior that are often an early warning sign of failure. This allows for early troubleshooting, planning and appropriate intervention by maintenance teams. Using the predictive analytics software solution, an MOH interval extension was implemented for an integrally geared centrifugal IA compressor installed at an ADNOC Gas Processing site. The compressor was due for MOH at its traditional fixed maintenance interval of 40,000 running hours in Nov 2019. Through this approach, the actual performance and condition of the compressor was assessed. Its process and equipment parameters (i.e. casing vibrations, bearing vibrations, bearing temperatures and lube oil supply temperature/pressure, etc.) were reviewed, which did not flag any abnormality. The compressor's performance had not deviated from the historical norm; indicating that the equipment was in a healthy condition and had no signs of performance degradation. With this insight, a 15 months extension of the MOH was achieved. Furthermore, a 30% maintenance cost reduction throughout the compressor's life cycle is projected while ensuring equipment's reliability and integrity are upheld. A total of 7 days maintenance down time including work force and materials planning for the MOH activities was deferred. The equipment remained in operation until its rescheduled date for MOH. Through the deployment of predictive analytics solutions, informed decisions can be made by maintenance professionals to challenge traditional maintenance practices, increase Mean Time Between Overhauls (MBTO), realize the full potential of a plant's process & utilities machinery and optimize operational costs of plant assets.

Technical and Engineering Decisions That Drive Choices for Predictive Analytics of Plant Data

2011 ◽  
Author(s):  
William Nieman
Keyword(s):  
Real Time ◽  
Predictive Analytics ◽  
Current Data ◽  
Operating Conditions ◽  
Maintenance Cost ◽  
Real Time Systems ◽  
Efficient Technology ◽  
Time Model ◽  
Trade Offs ◽  
Time Systems

Power generation has the goal of maximizing power output while minimizing operations and maintenance cost. The challenge for plant manager is to move closer to reliability limits while being confident the risks of any decision are understood. To attain their goals and meet this challenge they are coming to realize that they must have frequent, accurate assessment of equipment operating conditions, and a path to continued innovation-. At a typical plant, making this assessment involves the collection and effective analysis of reams of complex, interrelated production system data, including demand requirements, load, ambient temperature, as well as the dependent equipment data. Wind turbine health and performance data is available from periodic and real-time systems. To obtain the timeliest understanding of equipment health for all the key resources in a large plant or fleet, engineers increasingly turn to real-time, model-based solutions. Real-time systems are capable of creating actionable intelligence from large amounts and diverse sources of current data. They can automatically detect problems and provide the basis for diagnosis and prioritization effectively for many problems, and they can make periodic inspection methods much more efficient. Technology exists to facilitate prediction of when assets will fail, allowing engineers to target maintenance costs more effectively. But, it is critical to select the best predictive analytics for your plant. How do you make that choice correctly? Real-time condition monitoring and analysis tools need to be matched to engineering process capability. Tools are employed at the plant in lean, hectic environments; others are deployed from central monitoring centers charged with concentrating scarce resources to efficiently support plants. Applications must be flexible and simple to implement and use. Choices made in selection of new tools can be very important to future success of plant operations. So, these choices require solid understanding of the problems to be solved and the advantages and trade-offs of potential solutions. This choice of the best Predictive Analytic solution will be discussed in terms of key technology elements and key engineering elements.

scholarly journals PrimaVera: Synergising Predictive Maintenance

2020 ◽  
Vol 10 (23) ◽  
pp. 8348
Author(s):  
Bram Ton ◽  
Rob Basten ◽  
John Bolte ◽  
Jan Braaksma ◽  
Alessandro Di Bucchianico ◽  
...  
Keyword(s):  
Asset Management ◽  
Process Model ◽  
Building Blocks ◽  
Predictive Maintenance ◽  
Full Potential ◽  
Support Tools ◽  
Maintenance Cycle ◽  
Optimal Maintenance ◽  
Organisational Factors ◽  
Maintenance Process

The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.

scholarly journals PV System Predictive Maintenance: Challenges, Current Approaches, and Opportunities

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1398 ◽  
Author(s):  
Lisa B. Bosman ◽  
Walter D. Leon-Salas ◽  
William Hutzel ◽  
Esteban A. Soto
Keyword(s):  
Solar Energy ◽  
The United States ◽  
Predictive Maintenance ◽  
Third Party ◽  
System Level ◽  
Pv System ◽  
Actual Performance ◽  
Call To Action ◽  
Expected Performance ◽  
Solar Energy Industry

Within the United States solar energy industry, there is a general motto of “set it and forget it” with solar energy. This notion is derived from much of the research and reliability studies around the photovoltaic (PV) panels themselves, not necessarily the PV system as a whole (including the inverter and other components). This implies that maintenance and regular monitoring is not needed. Yet many things can go wrong to cause the actual performance to deviate from the expected performance. If failures and/or unanticipated degradation issues go undetected, they will lead to reduced energy generation (and associated electricity credits) and/or potential loss of component warranty because of manufacturer turnover. Given the size of the problem and gaps with current solutions, the authors propose that PV system owners need an unbiased third-party off-the-shelf system-level predictive maintenance tool to optimize return-on-investment and minimize time to warranty claim in PV installations. This paper reviews the literature highlighting challenges, current approaches, and opportunities for PV predictive maintenance. The paper concludes with a call to action for establishing a collaborative agenda toward prioritizing PV predictive maintenance.

scholarly journals A data-driven predictive maintenance strategy based on accurate failure prognostics

2021 ◽  
Vol 23 (2) ◽  
pp. 387-394
Author(s):  
Chuang Chen ◽  
Cunsong Wang ◽  
Ningyun Lu ◽  
Bin Jiang ◽  
Yin Xing
Keyword(s):  
Predictive Maintenance ◽  
Data Driven ◽  
Maintenance Cost ◽  
Maintenance Strategy ◽  
Engineering Systems ◽  
Data Set ◽  
Periodic Maintenance ◽  
Maintenance Decisions ◽  
Aero Engines ◽  
Failure Prognostics

Maintenance is fundamental to ensure the safety, reliability and availability of engineering systems, and predictive maintenance is the leading one in maintenance technology. This paper aims to develop a novel data-driven predictive maintenance strategy that can make appropriate maintenance decisions for repairable complex engineering systems. The proposed strategy includes degradation feature selection and degradation prognostic modeling modules to achieve accurate failure prognostics. For maintenance decision-making, the perfect time for taking maintenance activities is determined by evaluating the maintenance cost online that has taken into account of the failure prognostic results of performance degradation. The feasibility and effectiveness of the proposed strategy is confirmed using the NASA data set of aero-engines. Results show that the proposed strategy outperforms the two benchmark maintenance strategies: classical periodic maintenance and emerging dynamic predictive maintenance.

scholarly journals Prediction of Belt Drive Faults in Case of Predictive Maintenance in Industry 4.0 Platform

2021 ◽  
Vol 11 (21) ◽  
pp. 10307
Author(s):  
Artur Pollak ◽  
Sebastian Temich ◽  
Wojciech Ptasiński ◽  
Jacek Kucharczyk ◽  
Damian Gąsiorek
Keyword(s):  
Continuous Production ◽  
Predictive Maintenance ◽  
Production Costs ◽  
Maintenance Cost ◽  
Belt Drive ◽  
Manufacturing Companies ◽  
Maintenance System ◽  
Remaining Service Life ◽  
Reduced Costs ◽  
Belt Transmission

Continuous production maintenance cost is among one of the highest operational expenses for manufacturing companies. Proper planning of maintenance interventions results in optimized equipment use, higher product quality, and reduced costs. For a belt drive usefulness, it is important that it is properly stretched and has no defects. However, manual condition assessment requires a production line stop, which in turn causes production to stop with associated consequences. Continuous fault diagnosis for anomalies is a fundamental step in estimating a component’s remaining service life and then obtaining a reliable predictive maintenance system that reduces production costs. The presented work presents an approach to anomaly detection based on the vibrations obtained from the operation of the belt transmission.

scholarly journals The Use of Predictive Maintenance in the Production Processes

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Aneta NAPIERAJ
Keyword(s):  
Data Analysis ◽  
Production Process ◽  
Preventive Maintenance ◽  
Cost Reduction ◽  
Predictive Maintenance ◽  
Acceptable Level ◽  
Safety Requirements ◽  
Production Processes ◽  
Machine Reliability ◽  
Level Of Risk

Failures are a problem for every company that causes the plant to stop working and thus incur losses. It is therefore obvious thatcompanies want to eliminate unplanned downtime in the production process. In the wake of the still increasing demands in termsof productivity and safety requirements, cost reduction, the industry is forced to seek the optimum between economic requirementsand an acceptable level of risk in terms of security. Modern factories equipped with computerized processes and extensive diagnostictools often do not use all the information that is collected from the hardware level. It happens that some of the relationshipsbetween events are often overlooked or neglected.The article presents an approach to increasing machine reliability through predictive data analysis. The assumptions of the predictiveand preventive maintenance methods are presented. The threats and possibilities offered by this methodology implemented inthe production process are presented.

scholarly journals Estimating Producers’ Preferences for Sod Attributes: A Combined Approach of Discrete Choice Experiments and Eye-tracking Technology

HortScience ◽  
2020 ◽  
Vol 55 (10) ◽  
pp. 1589-1596
Author(s):  
Candi Ge ◽  
Chanjin Chung ◽  
Tracy A. Boyer ◽  
Marco Palma
Keyword(s):  
Drought Tolerance ◽  
Eye Tracking ◽  
Discrete Choice ◽  
Salinity Tolerance ◽  
Cost Reduction ◽  
Shade Tolerance ◽  
Maintenance Cost ◽  
Willingness To Accept ◽  
Study Results ◽  
Tracking Technology

This study combines a discrete choice experiment and eye-tracking technology to investigate producers’ preferences for sod attributes including winterkill reduction, shade tolerance, drought tolerance, salinity tolerance, and maintenance cost reduction. Our study results show that sod producers valued drought tolerance the most, followed by shade tolerance, winterkill reduction, salinity tolerance, and lastly, a 10% maintenance cost reduction. Choice survey data revealed the existence of attribute non-attendance, i.e., respondents skipped some attributes, but statistical tests detected no clear evidence about the role of individuals’ attention changes on their willingness-to-accept estimates. Estimates using a scale heterogeneity multinomial logit model indicate an overall learning effect as respondents made choices in the survey. Producers’ willingness-to-accept were generally higher than consumers’ willingness-to-pay for the improved sod variety attributes, except for the drought tolerance attribute. However, the rankings for these attributes were the same between consumers and producers.

scholarly journals Predictive maintenance integrated production scheduling by applying deep generative prognostics models: approach, formulation and solution

2021 ◽  
Author(s):  
Simon Zhai ◽  
Meltem Göksu Kandemir ◽  
Gunther Reinhart
Keyword(s):  
Production Scheduling ◽  
Job Shop ◽  
Health Management ◽  
Mathematical Problem ◽  
Health Condition ◽  
Predictive Maintenance ◽  
Full Potential ◽  
Integrated Production ◽  
Operational Conditions ◽  
Simulated Condition

AbstractTo harness the full potential of predictive maintenance (PdM), PdM information has to be used to optimally plan production and maintenance actions. Hence, operation-specific modelling of degradation, i.e. predictions of the health condition under time-varying operational conditions, has to be realized. By utilizing operation-specific degradation information, maintenance and production can be planned with regard to each other and thus, predictive maintenance integrated production scheduling (PdM-IPS) is enabled. This publication proposes a novel PdM-IPS approach consisting of two interacting modules: an operation-specific Prognostics and Health Management (PHM) module and an integrated production scheduling and maintenance planning (IPSMP) module. Specifically, the mathematical problem of the IPSMP module based on an extended version of the maintenance integrated flexible job shop problem is formulated. A two-stage genetic algorithm to efficiently solve this problem is designed and subsequently applied to simulated condition monitoring, as well as real industrial data. Results indicate that the approach is able to find feasible high quality PdM integrated production schedules.

Sign in / Sign up

Export Citation Format

Share Document