scholarly journals Deep Auto-Encoder and Deep Forest-Assisted Failure Prognosis for Dynamic Predictive Maintenance Scheduling

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8373
Author(s):  
Hui Yu ◽  
Chuang Chen ◽  
Ningyun Lu ◽  
Cunsong Wang
Keyword(s):  
Decision Making ◽  
Maintenance Scheduling ◽  
Predictive Maintenance ◽  
Advanced Technology ◽  
Sensor Data ◽  
Deep Forest ◽  
Maintenance Decisions ◽  
Maintenance Decision ◽  
Failure Prognosis ◽  
Failure Probabilities

Prognostics and health management (PHM) with failure prognosis and maintenance decision-making as the core is an advanced technology to improve the safety, reliability, and operational economy of engineering systems. However, studies of failure prognosis and maintenance decision-making have been conducted separately over the past years. Key challenges remain open when the joint problem is considered. The aim of this paper is to develop an integrated strategy for dynamic predictive maintenance scheduling (DPMS) based on a deep auto-encoder and deep forest-assisted failure prognosis method. The proposed DPMS method involves a complete process from performing failure prognosis to making maintenance decisions. The first step is to extract representative features reflecting system degradation from raw sensor data by using a deep auto-encoder. Then, the features are fed into the deep forest to compute the failure probabilities in moving time horizons. Finally, an optimal maintenance-related decision is made through quickly evaluating the costs of different decisions with the failure probabilities. Verification was accomplished using NASA’s open datasets of aircraft engines, and the experimental results show that the proposed DPMS method outperforms several state-of-the-art methods, which can benefit precise maintenance decisions and reduce maintenance costs.

Key Techniques in Intelligent Predictive Maintenance (IPdM) – A Framework of Intelligent Faults Diagnosis and Prognosis System (IFDaPS)

2014 ◽  
Vol 1039 ◽  
pp. 490-505 ◽  
Author(s):  
Ke Sheng Wang
Keyword(s):  
Decision Making ◽  
Frequency Domain ◽  
Wavelet Packet ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Ann Model ◽  
Diagnosis And Prognosis ◽  
Faults Diagnosis ◽  
Maintenance Decision

Intelligent predictive maintenance (IPdM) is a maintenance strategy that makes maintenance decisions automatically and dynamically based on Artificial Intelligence and Data mining techniques through condition monitoring of machines, equipment and production processes. IPdM system consists of the following main modules: sensor and data acquisition, signal and data processing, feature extractions, maintenance decision-making, key performance indicators, maintenance scheduling optimization and feedback control and compensation. Among them, the most important part of IPdM is maintenance decision-making, which includes diagnostics and prognostics. This paper proposes a framework of intelligent faults diagnosis and prognosis system (IFDaPS) and discuss some key technologies for implement IPdM policy in manufacturing and industries. A case study focus on the vibration signals collected from the sensors mounted on a pressure blower for critical components monitoring. We decompose the pre-processed signals into several signals using Wavelet Packet Decomposition (WPD), and then the signals are transformed to frequency domain using Fast Fourier Transform (FFT). The features extracted from frequency domain are used to train Artificial Neural Network (ANN). Trained ANN model is able to identify the fault of the components and predict its Remaining Useful Life (RUL). The case study demonstrates how to implement the proposed framework and intelligent technologies for IPdM and the result indicates its higher efficiency and effectiveness comparing to traditional methods.

scholarly journals Identification of the Criteria for Building Maintenance Decisions in Facility Management: First Step to Developing a Multi-Criteria Decision-Making Approach

Buildings ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 166
Author(s):  
Deniz Besiktepe ◽  
Mehmet E. Ozbek ◽  
Rebecca A. Atadero
Keyword(s):  
Decision Making ◽  
Condition Assessment ◽  
Facility Management ◽  
Current Status ◽  
Assessment Practices ◽  
Multi Criteria Decision Making ◽  
Building Maintenance ◽  
Maintenance Decisions ◽  
Maintenance Decision ◽  
Decision Making Model

Building maintenance is a fundamental practice in facility management, which supports the longevity of a building. Increasing costs of maintenance practices is a challenge for facility management professionals. Given that, building maintenance decisions often comprise complex and conflicting criteria. The primary purpose of this study is to develop and rank a set of criteria needed for constructing a multi-criteria decision-making model for use in building maintenance processes. This study also has an exploratory aspect and tries to establish the decision-making and condition assessment practices currently used in facility management. To do so, a literature review was conducted to reveal the significant criteria for building maintenance decision-making processes. Moreover, the results of a nationwide survey conducted with the members of two globally recognized facility management organizations were utilized. Identified criteria address a gap in facilities management research, i.e., the lack of comprehensive criteria in building maintenance decision-making, and can be used for the development of a multi-criteria decision-making model for use in building maintenance processes. Furthermore, the results of this study can help establish the current status of decision-making and condition assessment practices in facility management.

scholarly journals Bayesian maintenance decision optimisation based on computing the information value from condition inspections

2021 ◽  
pp. 1748006X2097812
Author(s):  
Guang Zou ◽  
Kian Banisoleiman ◽  
Arturo González
Keyword(s):  
Decision Making ◽  
Structural Integrity ◽  
Fatigue Cracking ◽  
Cost Effective ◽  
Offshore Structures ◽  
Information Value ◽  
Bayesian Decision ◽  
Maintenance Strategies ◽  
Optimal Maintenance ◽  
Maintenance Decision

A challenge in marine and offshore engineering is structural integrity management (SIM) of assets such as ships, offshore structures, mooring systems, etc. Due to harsh marine environments, fatigue cracking and corrosion present persistent threats to structural integrity. SIM for such assets is complicated because of a very large number of rewelded plates and joints, for which condition inspections and maintenance are difficult and expensive tasks. Marine SIM needs to take into account uncertainty in material properties, loading characteristics, fatigue models, detection capacities of inspection methods, etc. Optimising inspection and maintenance strategies under uncertainty is therefore vital for effective SIM and cost reductions. This paper proposes a value of information (VoI) computation and Bayesian decision optimisation (BDO) approach to optimal maintenance planning of typical fatigue-prone structural systems under uncertainty. It is shown that the approach can yield optimal maintenance strategies reliably in various maintenance decision making problems or contexts, which are characterized by different cost ratios. It is also shown that there are decision making contexts where inspection information doesn’t add value, and condition based maintenance (CBM) is not cost-effective. The CBM strategy is optimal only in the decision making contexts where VoI > 0. The proposed approach overcomes the limitation of CBM strategy and highlights the importance of VoI computation (to confirm VoI > 0) before adopting inspections and CBM.

scholarly journals STANDARDISING MAINTENANCE JOBS TO IMPROVE GROUPING DECISION MAKING

2021 ◽  
Vol 1 ◽  
pp. 2701-2710
Author(s):  
Julie Krogh Agergaard ◽  
Kristoffer Vandrup Sigsgaard ◽  
Niels Henrik Mortensen ◽  
Jingrui Ge ◽  
Kasper Barslund Hansen ◽  
...  
Keyword(s):  
Decision Making ◽  
Tacit Knowledge ◽  
True Variance ◽  
Complex Decision ◽  
Maintenance Decision ◽  
Maintenance Activities

AbstractMaintenance decision making is an important part of managing the costs, effectiveness and risk of maintenance. One way to improve maintenance efficiency without affecting the risk picture is to group maintenance jobs. Literature includes many examples of algorithms for the grouping of maintenance activities. However, the data is not always available, and with increasing plant complexity comes increasingly complex decision requirements, making it difficult to leave the decision making up to algorithms.This paper suggests a framework for the standardisation of maintenance data as an aid for maintenance experts to make decisions on maintenance grouping. The standardisation improves the basis for decisions, giving an overview of true variance within the available data. The goal of the framework is to make it simpler to apply tacit knowledge and make right decisions.Applying the framework in a case study showed that groups can be identified and reconfigured and potential savings easily estimated when maintenance jobs are standardised. The case study enabled an estimated 7%-9% saved on the number of hours spent on the investigated jobs.

Application of Improved Genetic Algorithm in Maintenance Decision for Turbine-Generator Unit

2010 ◽  
Vol 44-47 ◽  
pp. 2940-2944
Author(s):  
Qing He ◽  
Jian Ding Zhang
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithms ◽  
Maintenance Scheduling ◽  
Improved Genetic Algorithm ◽  
Turbine Generator ◽  
Executive Process ◽  
Generator Unit ◽  
Maintenance Decision ◽  
Constrained Conditions ◽  
Operating Unit

The complicated function relations are more prone to appear in the maintenance scheduling of steam-turbine generator unit. Many constrained conditions are often attendant with these function relations. In these situations, the traditional method often can not obtain the exact value. The genetic algorithm (GA), a kind of the heuristic algorithms, does not need the function own good analytic properties. In addition, as the operating unit of GA is the group, so it applies to the parallel computing process. In GA executive process, the offspring continually inherit the genes from the parents, so it is more prone to be involved in the local convergence. An improved genetic algorithm is proposed and used in the model of maintenance decision of turbine-generator unit under. The goal of the model is to seek to the rational maintenance scheduling of the generator unit, so as to minimize the sum of the maintenance expense, the loss of the profit on the generated energy, and the loss of the penalty. It is proved by the example that IGA is highly efficient.

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