Fleet Prognosis with Physics-informed Recurrent Neural Networks

Services and warranties of large fleets of engineering assets is a very profitable business. The success of companies in that area is often related to predictive maintenance driven by advanced analytics. Therefore, accurate modeling, as a way to understand how the complex interactions between operating conditions and component capability define useful life, is key for services profitability. Unfortunately, building prognosis models for large fleets is a daunting task as factors such as duty cycle variation, harsh environments, inadequate maintenance, and problems with mass production can lead to large discrepancies between designed and observed useful lives. This paper introduces a novel physics-informed neural network approach to prognosis by extending recurrent neural networks to cumulative damage models. We propose a new recurrent neural network cell designed to merge physics-informed and data-driven layers. With that, engineers and scientists have the chance to use physics-informed layers to model parts that are well understood (e.g., fatigue crack growth) and use data-driven layers to model parts that are poorly characterized (e.g., internal loads). A simple numerical experiment is used to present the main features of the proposed physics-informed recurrent neural network for damage accumulation. The test problem consist of predicting fatigue crack length for a synthetic fleet of airplanes subject to different mission mixes. The model is trained using full observation inputs (far-field loads) and very limited observation of outputs (crack length at inspection for only a portion of the fleet). The results demonstrate that our proposed hybrid physics-informed recurrent neural network is able to accurately model fatigue crack growth even when the observed distribution of crack length does not match with the (unobservable) fleet distribution.Comment: Data and codes (including our implementation for both the multi-layer perceptron, the stress intensity and Paris law layers, the cumulative damage cell, as well as python driver scripts) used in this manuscript are publicly available on GitHub at https://github.com/PML-UCF/pinn. The data and code are released under the MIT Licens

Uncertainty-aware deep learning for prediction of remaining useful life of mechanical systems

Remaining useful life (RUL) prediction is a problem that researchers in the prognostics and health management (PHM) community have been studying for decades. Both physics-based and data-driven methods have been investigated, and in recent years, deep learning has gained significant attention. When sufficiently large and diverse datasets are available, deep neural networks can achieve state-of-the-art performance in RUL prediction for a variety of systems. However, for end users to trust the results of these models, especially as they are integrated into safety-critical systems, RUL prediction uncertainty must be captured. This work explores an approach for estimating both epistemic and heteroscedastic aleatoric uncertainties that emerge in RUL prediction deep neural networks and demonstrates that quantifying the overall impact of these uncertainties on predictions reveal valuable insight into model performance. Additionally, a study is carried out to observe the effects of RUL truth data augmentation on perceived uncertainties in the model

Machine learning approach using MLP and SVM algorithms for the fault prediction of a centrifugal pump in the oil and gas industry

The demand for cost-effective, reliable and safe machinery operation requires accurate fault detection and classification to achieve an efficient maintenance strategy and increase performance. Furthermore, in strategic sectors such as the oil and gas industry, fault prediction plays a key role to extend component lifetime and reduce unplanned equipment thus preventing costly breakdowns and plant shutdowns. This paper presents the preliminary development of a simple and easy to implement machine learning (ML) model for early fault prediction of a centrifugal pump in the oil and gas industry. The data analysis is based on real-life historical data from process and equipment sensors mounted on the selected machinery. The raw sensor data, mainly from temperature, pressure and vibrations probes, are denoised, pre-processed and successively coded to train the model. To validate the learning capabilities of the ML model, two different algorithms-the Support Vector Machine (SVM) and the Multilayer Perceptron (MLP)-are implemented in KNIME platform. Based on these algorithms, potential faults are successfully recognized and classified ensuring good prediction accuracy. Indeed, results from this preliminary work show that the model allows us to properly detect the trends of system deviations from normal operation behavior and generate fault prediction alerts as a maintenance decision support system for operatives, aiming at avoiding possible incoming failures

Intelligent Condition Monitoring and Prognostic Methods with Applications to Dynamic Seals in the Oil & Gas Industry

The capital-intensive oil & gas industry invests billions of dollars in equipment annually and it is important to keep the equipment in top operating condition to help maintain efficient process operations and improve the rate of return by predicting failures before incidents. Digitalization has taken over the world with advances in sensor technology, wireless communication and computational capabilities, however oil & gas industry has not taken full advantage of this despite being technology centric. Dynamic seals are a vital part of reciprocating and rotary equipment such as compressor, pumps, engines, etc. and are considered most frequently failing component. Polymeric seals are increasingly complex and non-linear in behavior and have been the research of interest since 1950s. Most of the prognostic studies on seals are physics-based and requires direct estimation of different physical parameters to assess the degradation of seals, which are often difficult to obtain during operation. Another feasible approach to predict the failure is from performance related sensor data and is termed as data-driven prognostics. The offline phase of this approach is where the performance related data from the component of interest are acquired, pre-processed and artificial intelligence tools or statistical methods are used to model the degradation of a system. The developed models are then deployed online for a real-time condition monitoring. There is a lack of research on the data-driven based tools and methods for dynamic seal prognosis. The primary goal in this dissertation is to develop offline data-driven intelligent condition monitoring and prognostic methods for two types of dynamic seals used in the oil & gas industry, to avoid fatal breakdown of rotary and reciprocating equipment. Accordingly, the interest in this dissertation lies in developing models to effectively evaluate and classify the running condition of rotary seals; assess the progression of degradation from its incipient to failure and to estimate the remaining useful life (RUL) of reciprocating seals. First, a data-driven prognostic framework is developed to classify the running condition of rotary seals. An accelerated aging and testing procedure simulating rotary seal operation in oil field is developed to capture the behavior of seals through their cycle of operation until failure. The diagnostic capability of torque, leakage and vibration signal in differentiating the health states of rotary seals using experiments are compared. Since the key features that differentiate the health condition of rotary seals are unknown, an extensive feature extraction in time and frequency domain is carried out and a wrapper-based feature selection approach is used to select relevant features, with Multilayer Perceptron neural network utilized as classification technique. The proposed approach has shown that features extracted from torque and leakage lack a better discriminating power on its own, in classifying the running condition of seals throughout its service life. The classifier built using optimal set of features from torque and leakage collectively has resulted in a high classification accuracy when compared to random forest and logistic regression, even for the data collected at a different operating condition. Second, a data-driven approach to predict the degradation process of reciprocating seals based on friction force signal using a hybrid Particle Swarm Optimization - Support Vector Machine is presented. There is little to no knowledge on the feature that reflects the degradation of reciprocating seals and on the application of SVM in predicting the future running condition of polymeric components such as seals. Controlled run-to-failure experiments are designed and performed, and data collected from a dedicated experimental set-up is used to develop the proposed approach. A degradation feature with high monotonicity is used as an indicator of seal degradation. The pseudo nearest neighbor is used to determine the essential number of inputs for forecasting the future trend. The most challenging aspect of tuning parameters in SVM is framed in terms of an optimization problem aimed at minimizing the prediction error. The results indicate the effectiveness and better accuracy of the proposed approach when compared to GA-SVM and XGBoost. Finally, a deep neural network-based approach for estimating remaining useful life of reciprocating seals, using force and leakage signals is presented. Time domain and frequency domain statistical features are extracted from the measurements. An ideal prognostic feature should be well correlated with degradation time, monotonically increasing or decreasing and robust to outliers. The identified metrics namely: monotonicity, correlation and robustness are used to evaluate the goodness of extracted features. Each of the three metric carries a relative importance in the RUL estimation and a weighted linear combination of the metrics are used to rank and select the best set of prognostic features. The redundancy in the selected features is eliminated using Kelley-Gardner-Sutcliffe penalty function-based correlation-clustering algorithm to select a representative feature from each of the clusters. Finally, RUL estimation is modeled using a deep neural network model. Run-to-failure data collected from a reciprocating set-up was used to validate this approach and the findings show that the proposed approach can improve the accuracy of RUL prediction when compared to PSO-SVM and XGBoost regression. This research has important contribution and implications to rotary and reciprocating seal domain in utilizing sensors along with machine learning algorithms in assessing the health state and prognosis of seals without any direct measurements. This research has paved the way to move from a traditional fail-and-fix to predict-and-prevent approach in maintenance of seals. The findings of this research are foundational for developing an online degradation assessment platform which can remotely monitor the performance degradation of seals and provide action recommendations on maintenance decisions. This would be of great interest to customers and oil field operators to improve equipment utilization, control maintenance cost by enabling just-in-time maintenance and increase rate of return on equipment by predicting failures before incidents

A Physics-informed Neural Network for Wind Turbine Main Bearing Fatigue

Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss). Unfortunately, historical data indicates that failure can happen far earlier than the component design lives. Root cause analysis investigations have pointed to problems inherent from manufacturing as the major contributor, as well as issues related to event loads (e.g., startups, shutdowns, and emergency stops), extreme environmental conditions, and maintenance practices, among others. Altogether, the multiple failure modes and contributors make modeling the remaining useful life of main bearings a very daunting task. In this paper, we present a novel physics-informed neural network modeling approach for main bearing fatigue. The proposed approach is fully hybrid and designed to merge physics-informed and data-driven layers within deep neural networks. The result is a cumulative damage model where the physics-informed layers are used model the relatively well-understood physics (L10 fatigue life) and the data-driven layers account for the hard to model components (i.e., grease degradation)

A novel condition monitoring methodology based on neural network of pump-turbines with extended operating range

Due to the entrance of new renewable energies, water-storage energy has to be regulated more frequently to keep the stability of power grid. Consequently, pump-turbines have to work under offdesign conditions more than before, which will cause more damage and decrease their useful life. Advanced monitoring methodologies that can balance the degradation of machine and revenues to the power plant has been required. To develop an innovative condition monitoring approach, vibration data was collected from different components of a pump-turbine which is running in an extended operating range. The consequences of operating range extension on the vibration of the pump-turbine have been studied by analysing the vibration signatures. The changing rule of the vibration behavior of the machine with the operating parameters has been obtained. An artificial neural network based model has been applied to build an autoregressive normal behavior model. The results indicated that the normal behavior model based on multi-layer neural net has the ability to predict the vibration characteristics of the machine in different operating conditions. This monitoring method can be adapted to the similar type of hydraulic turbine units.Peer ReviewedPostprint (published version

Data modelling and Remaining Useful Life estimation of rolls in a steel making cold rolling process

The economic cost of roll refurbishment in the steel-making industry is considerable. In a cold rolling mill, wear and damage of rolls disrupt the industrial environment, so it is critical to predict the remaining useful life early and change the roll without causing disruption to the manufacturing process. However, since cold rolling is a complex process affected by multiple variables which are operated in adverse conditions, it is very challenging to mathematically analyse the roll wear and failure. For this reason, in the present paper, a data-driven solution is proposed to predict the correct time for changing individual rolls. To develop an accurate predictive model, several datasets containing high-resolution production data and roll refurbishment data collected from a UK based steel plant have been acquired and processed in a way that the roll wear is modelled as a Remaining Useful Life (RUL) problem, where the number of coils that a roll is able to process is viewed as the remaining cycles. Then hybrid deep learning models are used to predict the Remaining Useful Life of rolls used in steel making. This novel data-driven approach achieves high prediction accuracy and has been validated on a real-world dataset. The proposed approach not only helps avoiding early failure but also can serve as a critical step towards the design of an optimal, automated maintenance schedule for the roll management