Residual useful life prediction of large-size low-speed slewing bearings – a data driven method

In this paper, a data driven method based on Ensemble Empirical Mode Decomposition-Principal Component Analysis (EEMD-PCA) and Least Square-Support Vector Machine (LS-SVM) is proposed to achieve residual useful life (RUL) prediction for slewing bearings. Firstly, life-cycle vibration signals are acquired and divided into several signal segments, and EEMD is then utilized to decompose each segment into Intrinsic Mode Functions (IMFs). Afterwards, PCA is introduced to illustrate the trends of each IMF across the life cycle, and some of the IMFs that contribute to reveal the performance degradation process of the slewing bearing are selected to reconstruct and de-noise the vibration signals. After that, continuous squared prediction error (C-SPE) and its features are presented as performance degradation indicators. Finally, an RUL prediction model is built on the basis of the indicators using LS-SVM. The results of a life-cycle experiment show that the C-SPE of the de-noised vibration signals can precisely explain the performance degradation process of the tested slewing bearing and that the established RUL prediction model is close to practice. Besides, a comparison study shows that the C-SPE based RUL prediction model is more efficient and accurate than the signal based model. Therefore, the proposed method ensures high-reliability slewing bearing prognostics

PROGNOSTIC ALGORITHM DEVELOPMENT FOR PLANT MONITORING AND MAINTENANCE PLANNING

The economic goals in a typical industrial plant are to improve product quality, maximize equipment up-time, reliability, and availability, and minimize spare part inventories and maintenance costs. Modern facilities are comprised of thousands of subsystems with critical unique components. Simple components and more complex engineering systems alike are typically engineered to perform satisfactorily. Their lives can be predicted under normal operation runtime. It should be the same with chronological time lapse from the moment of installation. However, their ages accelerate faster than chronological time lapse if they are operated under unfavorable working conditions, making their remaining life predictions likely not accurate, thus making failure imminent. These components most become more sophisticated and advanced to meet supercritical demands, and unplanned critical failures of any these components can result in costly operation stoppages. Speedy repair costs of failed components during operation can be extremely costly, not only due to the failed component, but also to collateral damage to other components, which can result in significant economic loss, lost production, personal injury, and even loss of life. Today’s marketplace faces global competition, ever-changing customer perception, and evolving demand. Industrial plants are constantly retooling their operations and equipment to act in a supercritical manner, and this is happening amidst the already complex nature of mechanical structures, operational stress, and environmental influence. To address these continuous changes, early fault detection is imperative to accurately predict the Remaining Useful Life (RUL) of machinery to prevent performance degradation and malfunction, which leads to substantial damage. Predicting the RUL of degraded components and putting these components to use will reduce spare part inventories and maintenance and increase reliability, availability, and performance to minimize plant downtime and production loss while enhancing operation safety. The primary purpose of this dissertation is to create an improved prognostic algorithm and methodology to predict the time of machinery failure. Empirical wear models built using historical operating conditions are then used to monitor the RUL of machinery and components. Machinery online monitoring data are used to determine the current health state of components along their life curves

Deep Learning for predictive maintenance

Recently, with the appearance of Industry 4.0 (I4.0), machine learning (ML) within artificial intelligence (AI), industrial Internet of things (IIoT) and cyber-physical system (CPS) have accelerated the development of a data-orientated applications such as predictive maintenance (PdM). PdM applied to asset-dependent industries has led to operational cost savings, productivity improvements and enhanced safety management capabilities. In addition, predictive maintenance strategies provide useful information concerning the source of the failure or malfunction, reducing unnecessary maintenance operations. The concept of prognostics and health management (PHM) has appeared as a predictive maintenance process. PHM has become an unavoidable tendency in smart manufacturing to offer a reliable solution for handling industrial equipment’s health status. This later requires efficient and effective system health monitoring methods, including processing and analysing massive machinery data to detect anomalies and perform diagnosis and prognosis. Prognostics is considered a key PHM process with capabilities for predicting future states, mainly based on predicting the residual lifetime during which a machine can perform its intended function, i.e., estimating the remaining useful life (RUL) of a system. The prognostic research domain is far from being mature, which is still new and explains the various challenges that must be addressed. Therefore, the work presented in this thesis will mainly focus on the prognostic of monitored machinery from an RUL estimation point of view using Deep Learning (DL) algorithms. Capitalising on the recent success of the DL, this dissertation introduces methods and algorithms dedicated to predictive maintenance. We focused on improving the performance of aero-engine prognostic, particularly in estimating an accurate RUL using ensemble learning and deep learning. To this end, two contributions have been proposed, and the results obtained were validated by an extensive comparative analysis using public C-MAPSS turbofan engine benchmark datasets. The first contribution, for RUL predictions, we proposed two-hybrid methods based on the promising DL architectures to leverage the power of the multimodal and hybrid deep neural network in order to capture various information at different time intervals and ultimately achieve more accurate RUL predictions. The proposed end-to-end deep architectures jointly optimise the feature reduction and RUL prediction steps in a hierarchical manner, intending to achieve data representation in low dimensionality and minimal variable redundancy while preserving critical asset degradation information with minimal preprocessing effort. The second contribution, in a practical situation, RUL is usually affected by uncertainty. Therefore, we proposed an innovative RUL estimation strategy that assesses degrading machinery’s health status (provides the probabilities of system failure in different time windows) and provides the prediction of RUL window. Keywords: Prognostics and Health Management (PHM), Remaining useful life (RUL), Predictive Maintenance (PdM), C-MAPSS dataset, Ensemble learning, Deep learnin

Practical Methods for Optimizing Equipment Maintenance Strategies Using an Analytic Hierarchy Process and Prognostic Algorithms

Many large organizations report limited success using Condition Based Maintenance (CbM). This work explains some of the causes for limited success, and recommends practical methods that enable the benefits of CbM. The backbone of CbM is a Prognostics and Health Management (PHM) system. Use of PHM alone does not ensure success; it needs to be integrated into enterprise level processes and culture, and aligned with customer expectations. To integrate PHM, this work recommends a novel life cycle framework, expanding the concept of maintenance into several levels beginning with an overarching maintenance strategy and subordinate policies, tactics, and PHM analytical methods. During the design and in-service phases of the equipment’s life, an organization must prove that a maintenance policy satisfies specific safety and technical requirements, business practices, and is supported by the logistic and resourcing plan to satisfy end-user needs and expectations. These factors often compete with each other because they are designed and considered separately, and serve disparate customers. This work recommends using the Analytic Hierarchy Process (AHP) as a practical method for consolidating input from stakeholders and quantifying the most preferred maintenance policy. AHP forces simultaneous consideration of all factors, resolving conflicts in the trade-space of the decision process. When used within the recommended life cycle framework, it is a vehicle for justifying the decision to transition from generalized high-level concepts down to specific lower-level actions. This work demonstrates AHP using degradation data, prognostic algorithms, cost data, and stakeholder input to select the most preferred maintenance policy for a paint coating system. It concludes the following for this particular system: A proactive maintenance policy is most preferred, and a predictive (CbM) policy is more preferred than predeterminative (time-directed) and corrective policies. A General Path prognostic Model with Bayesian updating (GPM) provides the most accurate prediction of the Remaining Useful Life (RUL). Long periods between inspections and use of categorical variables in inspection reports severely limit the accuracy in predicting the RUL. In summary, this work recommends using the proposed life cycle model, AHP, PHM, a GPM model, and embedded sensors to improve the success of a CbM policy

A systematic mapping of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector

The increasing availability of data, gathered by sensors and intelligent machines, is chang-ing the way decisions are made in the manufacturing sector. In particular, based on predictive approach and facilitated by the nowadays growing capabilities of hardware, cloud-based solutions, and new learning approaches, maintenance can be scheduled—over cell engagement and resource monitoring—when required, for minimizing (or managing) unexpected equipment failures, improving uptime through less aggressive maintenance schedules, shortening unplanned downtime, reducing excess (direct and indirect) cost, reducing long-term damage to machines and processes, and improve safety plans. With access to increased levels of data (and over learning mechanisms), companies have the capability to conduct statistical tests using machine learning algorithms, in order to uncover root causes of problems previously unknown. This study analyses the maturity level and contributions of machine learning methods for predictive maintenance. An upward trend in publications for predictive maintenance using machine learning techniques was identified with the USA and China leading. A mapping study—steady set until early 2019 data—was employed as a formal and well-structured method to synthesize material and to report on pervasive areas of research. Type of equipment, sensors, and data are mapped to properly assist new researchers in positioning new research activities in the domain of smart maintenance. Hence, in this paper, we focus on data-driven methods for predictive maintenance (PdM) with a comprehensive survey on applications and methods until, for the sake of commenting on stable proposal, 2019 (early included). An equal repartition between evaluation and validation studies was identified, this being a symptom of an immature but growing research area. In addition, the type of contribution is mainly in the form of models and methodologies. Vibrational signal was marked as the most used data set for diagnosis in manufacturing machinery monitoring; furthermore, supervised learning is reported as the most used predictive approach (ensemble learning is growing fast). Neural networks, followed by random forests and support vector machines, were identified as the most applied methods encompassing 40% of publications, of which 67% related to deep neural network with long short-term memory predominance. Notwithstanding, there is no robust approach (no one reported optimal performance over different case tests) that works best for every problem. We finally conclude the research in this area is moving fast to gather a separate focused analysis over the last two years (whenever stable implementations will appear)

Data-driven prognostics for critical electronic assemblies and electromechanical components

The industrial digitalisation enables the adoption of robust, data-driven maintenance strategies that increase safety and reliability of critical assets such as electronics. And yet, an implementation of data-driven methods which primarily address the industrialisation of diagnostic and prognostic strategies is opposed by various, application specific challenges. This thesis collates such restricting factors encountered within the oil and gas industry, in particular for the critical electrical systems and components in upstream deep drilling tools. A fleet-level, tuned machine learning approach is presented that classifies the operational state (no-failure/ failure) of downhole tool printed circuit board assemblies. It supports maintenance decision making under varying levels of failure costs and fleet reliability scenarios. Applied within a maintenance scheme it has the potential to minimise non-productive time while increasing operational reliability. Likewise, a tailored and efficient deep learning data pipeline is proposed for a component-level forecast of the end of life of electromagnetic relays. It is evaluated using high resolution life-cycle data which has been collected as a part of this thesis. In combination with a failure analysis, the proposed method improves the prognostics capabilities compared to traditional methods which have been proposed so far in order to assess the operational health of electromagnetic relays. Two case studies underpin the need for tailored prognostic methods in order to provide viable solutions that can de-risk deep drilling operations. In consequence, the proposed approaches alleviate the pressure on current maintenance strategies which can no longer meet the stringent reliability requirements of upstream assets

Decision-Making for Utility Scale Photovoltaic Systems: Probabilistic Risk Assessment Models for Corrosion of Structural Elements and a Material Selection Approach for Polymeric Components

abstract: The solar energy sector has been growing rapidly over the past decade. Growth in renewable electricity generation using photovoltaic (PV) systems is accompanied by an increased awareness of the fault conditions developing during the operational lifetime of these systems. While the annual energy losses caused by faults in PV systems could reach up to 18.9% of their total capacity, emerging technologies and models are driving for greater efficiency to assure the reliability of a product under its actual application. The objectives of this dissertation consist of (1) reviewing the state of the art and practice of prognostics and health management for the Direct Current (DC) side of photovoltaic systems; (2) assessing the corrosion of the driven posts supporting PV structures in utility scale plants; and (3) assessing the probabilistic risk associated with the failure of polymeric materials that are used in tracker and fixed tilt systems. As photovoltaic systems age under relatively harsh and changing environmental conditions, several potential fault conditions can develop during the operational lifetime including corrosion of supporting structures and failures of polymeric materials. The ability to accurately predict the remaining useful life of photovoltaic systems is critical for plants ‘continuous operation. This research contributes to the body of knowledge of PV systems reliability by: (1) developing a meta-model of the expected service life of mounting structures; (2) creating decision frameworks and tools to support practitioners in mitigating risks; (3) and supporting material selection for fielded and future photovoltaic systems. The newly developed frameworks were validated by a global solar company.Dissertation/ThesisDoctoral Dissertation Civil and Environmental Engineering 201