Advanced Analytics for Predictive Maintenance with Limited Data: Exploring the Fouling Problem in Heat Exchanging Equipment

Abstract

The current oil and gas market is characterized by low prices, high uncertainties and a subsequent reduction in new investments. This leads to an ever-increasing attention towards more efficient asset management. The fouling effect is considered one of the main problems drastically affecting asset integrity/efficiency and heat exchanger performances of critical machineries in upstream production plants. This paper illustrates the application of advanced big data analytics and innovative machine learning techniques to face this challenge. The optimal maintenance scheduling and the early identification of workflow-blocking events strongly impact the overall production, as they heavily contribute to the reduction of down-times. While, machine learning techniques proved to introduce significant advantages to these problems, they are fundamentally data-driven. In industry scenarios, where dealing with a limited amount of data is standard practice, this means forcing the use of simpler models that are often not able to disentangle the real dynamics of the phenomenon. The lack of data is generally caused by frequent changes in operating conditions/field layout or an insufficient instrumentation system. Moreover, the intrinsic long duration of many physical phenomena and the ordinary asset maintenance lifecycle, cause a critical reduction in the number of relevant events that can be learned. In this work, the fouling problem has been explored leveraging only limited data. The attention is focused on two different equipment: heat exchangers and re-boilers. While the formers involve slower dynamics, the latter are characterized by a steady phase followed by an abrupt deterioration. Moreover, the first ones allow a proper scheduling of cleaning interventions in advance. On the other hand, the second forces a much quicker plant stop. Finally, heat exchangers are characterized by few episodes of comparable deterioration, while re-boilers present only a single episode. Regarding heat exchangers, a dual approach has been followed, merging a short-term, time-series-based model, and a long-term one based on linear regression. After having isolated a number of training regions related to the fouling episodes that showed a characteristic behavior, it is possible to obtain accurate results in the short-term and to capture the general trend in the long-term. In the case of re-boilers, a novelty detection approach has been adopted: first, the model learns the equipment normal behavior, then it uses the features learned to detect anomalies. This continuous training-predicting iteration also leverages the user feedback to adapt to new operating conditions. Results show that in an "young digital" industry, the use of limited data together with simpler machine learning techniques, can successfully become an automatic diagnostics tool supporting the operators to improve traditional maintenance activities as well as optimize the production rate, and finally the asset efficiency

Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities

“…Cadei et al [88] present an ensemble learning-based approach for fouling detection in heat exchanger equipment. The authors combine two approaches, a short-term approach using an Auto Regressive Integrated Moving Average (ARIMA) model and a long-term approach using a RIDGE model.…”

“…For example, one study [94] suggests training on lab-generated data may overcome the challenge. Another study proposes to utilize a one-class SVM [88] to reduce the need for labeled data, and another study exploits feature engineering [96] to develop higher-performing models. Overall, it was not the choice of ML algorithm, but primarily data quality, that impacted the performance the most, which is specifically challenging in fault diagnosis, as there is a severe lack of labeled data.…”

“…Overall, it was not the choice of ML algorithm, but primarily data quality, that impacted the performance the most, which is specifically challenging in fault diagnosis, as there is a severe lack of labeled data. GBR, TPOT Sensor failure Guelpa et al [87] Analytical Fouling Cadei et al [88] ARIMA, RIDGE, one-class SVM Fouling Kim et al [89] kM, MLP Fouling Park et al [90] RF Valves Langroudi et al [91] LR, DTR, RIDGE, kNN, PLS, SVM, RF, LASSO, XGBoost, ANN Pipes Bahlawan et al [92] Analytical Pipes Manservigi et al [93] Analytical Pipes Bode et al [94] LR, kNN, CART, RF, NB, SVM, ANN Multi-label Choi et al [96] AE, MLP Multi-label Li et al [95] kNN, RF, ANN, CNN Multi-label…”

“…Noteworthy is [88], as the authors were the only ones using a one-class SVM. This kind of approach solely needs a single class of data to find outliers, which might work well for fault detection.…”