A Predictive maintenance model for heterogeneous industrial refrigeration systems

The automatic assessment of the degradation state of industrial refrigeration systems is becoming increasingly important and constitutes a key-role within predictive maintenance approaches. Lately, data-driven methods especially became the focus of research in this respect. As they only rely on historical data in the development phase, they offer great advantages in terms of flexibility and generalisability by circumventing the need for specific domain knowledge. While most scientific contributions employ methods emerging from the field of machine learning (ML), only very few consider their applicability amongst different heterogeneous systems. In fact, the majority of existing contributions in this field solely apply supervised ML models, which assume the availability of labelled fault data for each system respectively. However, this places restrictions on the overall applicability, as data labelling is mostly conducted by humans and therefore constitutes a non-negligible cost and time factor. Moreover, such methods assume that all considered fault types occurred in the past, a condition that may not always be guaranteed to be satisfied. Therefore, this dissertation proposes a predictive maintenance model for industrial refrigeration systems by especially addressing its transferability onto different but related heterogeneous systems. In particular, it aims at solving a sub-problem known as condition-based maintenance (CBM) to automatically assess the system’s state of degradation. To this end, the model does not only estimate how far a possible malfunction has progressed, but also determines the fault type being present. As will be described in greater detail throughout this dissertation, the proposed model also utilises techniques from the field of ML but rather bypasses the strict assumptions accompanying supervised ML. Accordingly, it assumes the data of the target system to be primarily unlabelled while a few labelled samples are expected to be retrievable from the fault-free operational state, which can be obtained at low cost. Yet, to enable the model’s intended functionality, it additionally employs data from only one fully labelled source dataset and, thus, allows the benefits of data-driven approaches towards predictive maintenance to be further exploited. After the introduction, the dissertation at hand introduces the related concepts as well as the terms and definitions and delimits this work from other fields of research. Furthermore, the scope of application is further introduced and the latest scientific work is presented. This is then followed by the explanation of the open research gap, from which the research questions are derived. The third chapter deals with the main principles of the model, including the mathematical notations and the individual concepts. It furthermore delivers an overview about the variety of problems arising in this context and presents the associated solutions from a theoretical point of view. Subsequently, the data acquisition phase is described, addressing both the data collection procedure and the outcome of the test cases. In addition, the considered fault characteristics are presented and compared with the ones obtained from the related publicly available dataset. In essence, both datasets form the basis for the model validation, as discussed in the following chapter. This chapter then further comprises the results obtained from the model, which are compared with the ones retrieved from several baseline models derived from the literature. This work then closes with a summary and the conclusions drawn from the model results. Lastly, an outlook of the presented dissertation is provide

Automation and Control Architecture for Hybrid Pipeline Robots

The aim of this research project, towards the automation of the Hybrid Pipeline Robot (HPR), is the development of a control architecture and strategy, based on reconfiguration of the control strategy for speed-controlled pipeline operations and self-recovering action, while performing energy and time management. The HPR is a turbine powered pipeline device where the flow energy is converted to mechanical energy for traction of the crawler vehicle. Thus, the device is flow dependent, compromising the autonomy, and the range of tasks it can perform. The control strategy proposes pipeline operations supervised by a speed control, while optimizing the energy, solved as a multi-objective optimization problem. The states of robot cruising and self recovering, are controlled by solving a neuro-dynamic programming algorithm for energy and time optimization, The robust operation of the robot includes a self-recovering state either after completion of the mission, or as a result of failures leading to the loss of the robot inside the pipeline, and to guaranteeing the HPR autonomy and operations even under adverse pipeline conditions Two of the proposed models, system identification and tracking system, based on Artificial Neural Networks, have been simulated with trial data. Despite the satisfactory results, it is necessary to measure a full set of robot’s parameters for simulating the complete control strategy. To solve the problem, an instrumentation system, consisting on a set of probes and a signal conditioning board, was designed and developed, customized for the HPR’s mechanical and environmental constraints. As a result, the contribution of this research project to the Hybrid Pipeline Robot is to add the capabilities of energy management, for improving the vehicle autonomy, increasing the distances the device can travel inside the pipelines; the speed control for broadening the range of operations; and the self-recovery capability for improving the reliability of the device in pipeline operations, lowering the risk of potential loss of the robot inside the pipeline, causing the degradation of pipeline performance. All that means the pipeline robot can target new market sectors that before were prohibitive

Investigation of corrosion of carbon steel under insulation

Corrosion of metals under insulation is a serious concern for industries due to the fact that the insulation hides the metal from view which increases the likelihood of sudden failure. Carbon steel is one of the metal alloys frequently used in industries due to economic and technical reasons. However, it is quite susceptible to corrosion under insulation (CUI). The factors affecting corrosion of carbon steel under mineral wool insulation such as temperature, effectiveness of inhibitor, quantity and distribution of electrolyte in the insulation have not been extensively studied in the literature. In fact, studies on corrosion of metals under insulation are quite sparse compared to immersion (uninsulated) conditions. Therefore, the objectives of this study were to assess the effect of temperature (60 oC to 130 oC) on corrosion of carbon steel under insulation, effectiveness of a new commercial inhibitor (VpCI 619) in mitigating CUI of carbon steel, quantity and distribution of electrolyte (1wt. % NaCl) in mineral wool insulation as well as investigation of the drying times of the insulation using galvanic current and electrochemical impedance measurements. In addition, the prediction of CUI rate using Artificial Neural Network (ANN) was carried out with the aim of assessing the accuracy of prediction of different network parameters such as number of hidden layers, number of input parameters and choice of activation function. Prior to CUI studies, the water absorption capacity of mineral wool insulation was determined using standard procedures (ASTM C1511). This was carried out to assess the time it will take for the insulation to be saturated with water, the variability of repeated measurements as well as the total water content in the insulation. The CUI studies were carried out using a test rig that was based on ASTM G189-07 standard. The corrosion rates were estimated using weight loss technique and the effects of temperature, vapour phase inhibitor consisting primarily of sodium molybdate, quantity of electrolyte in insulation were investigated. The drying out profile of the insulation was assessed using galvanic current and electrochemical impedance measurements. Furthermore, the prediction of CUI rate was carried out using Artificial Neural Network and the effect of single and double hidden layers, sigmoid and hyperbolic tangent activation functions, as well as number of input parameters on accuracy of prediction of CUI rate were assessed. The results of the water absorption studies indicated continuous absorption of water even after immersion for 22 days. The water absorption capacity was greater for thermally treated insulation compared to untreated insulation samples due to thermal degradation of the oily additives and polymeric binders. The effect of temperature on CUI indicated an increase in corrosion rate from 60 oC to 80 oC. Further increase in temperature up to 130 oC resulted in a decrease in corrosion rate. The existence of a maximum point in the curve was attributed to the competing effects of two factors which include increased diffusivity of oxygen which dominates at low temperature and decreasing solubility of oxygen and insulation dry-out which dominates at temperatures exceeding 80 oC. The new commercial inhibitor was observed to mitigate the corrosion rate at the temperatures investigated in this study. The inhibition efficiency indicated an average of 89% when a dosage of 5.2 g/m2 of the inhibitor was used. The effectiveness was also observed to be dosage dependent with lower doses having less inhibition efficiency. The drying times of the insulation assessed using galvanic current and impedance methods were observed to decrease as temperature increased. The galvanic current was observed to decrease to zero while the impedance increased to high values as the insulation dries out. However, the drying times obtained from galvanic current method showed a higher variability compared to impedance method. The result of prediction of CUI rate using Artificial Neural Network indicated an increase in accuracy as the number of input parameters increased. Surprisingly, the accuracy of the predicted output from the four input parameters (temperature, dosage of inhibitor, quantity of electrolyte in insulation and sample position) was higher than the accuracy of the most influential parameters (temperature and dosage of inhibitor). This suggests that incorporation of more input parameters having some relationship with the output is more important in achieving a higher accuracy compared to using the most influential parameters only. In conclusion, this study indicated that mineral wool insulation absorbs water for a long period without saturation which increases the risk of CUI. Also, CUI rate increased with temperature up to 80 oC but decreased on further increase up to 130 oC. The new commercial inhibitor was effective in mitigating CUI at the temperatures investigated. Also, more test solution was observed at the lower part of the insulation compared to the upper part when installed on the CUI test rig which increases the risk of severe corrosion at the lower section of the insulation. The prediction of CUI rate using ANN indicated that inclusion of more input parameters could improve prediction accuracy. Moreover, the choice of activation functions also has effect on the accuracy of the predicted output.Corrosion of metals under insulation is a serious concern for industries due to the fact that the insulation hides the metal from view which increases the likelihood of sudden failure. Carbon steel is one of the metal alloys frequently used in industries due to economic and technical reasons. However, it is quite susceptible to corrosion under insulation (CUI). The factors affecting corrosion of carbon steel under mineral wool insulation such as temperature, effectiveness of inhibitor, quantity and distribution of electrolyte in the insulation have not been extensively studied in the literature. In fact, studies on corrosion of metals under insulation are quite sparse compared to immersion (uninsulated) conditions. Therefore, the objectives of this study were to assess the effect of temperature (60 oC to 130 oC) on corrosion of carbon steel under insulation, effectiveness of a new commercial inhibitor (VpCI 619) in mitigating CUI of carbon steel, quantity and distribution of electrolyte (1wt. % NaCl) in mineral wool insulation as well as investigation of the drying times of the insulation using galvanic current and electrochemical impedance measurements. In addition, the prediction of CUI rate using Artificial Neural Network (ANN) was carried out with the aim of assessing the accuracy of prediction of different network parameters such as number of hidden layers, number of input parameters and choice of activation function. Prior to CUI studies, the water absorption capacity of mineral wool insulation was determined using standard procedures (ASTM C1511). This was carried out to assess the time it will take for the insulation to be saturated with water, the variability of repeated measurements as well as the total water content in the insulation. The CUI studies were carried out using a test rig that was based on ASTM G189-07 standard. The corrosion rates were estimated using weight loss technique and the effects of temperature, vapour phase inhibitor consisting primarily of sodium molybdate, quantity of electrolyte in insulation were investigated. The drying out profile of the insulation was assessed using galvanic current and electrochemical impedance measurements. Furthermore, the prediction of CUI rate was carried out using Artificial Neural Network and the effect of single and double hidden layers, sigmoid and hyperbolic tangent activation functions, as well as number of input parameters on accuracy of prediction of CUI rate were assessed. The results of the water absorption studies indicated continuous absorption of water even after immersion for 22 days. The water absorption capacity was greater for thermally treated insulation compared to untreated insulation samples due to thermal degradation of the oily additives and polymeric binders. The effect of temperature on CUI indicated an increase in corrosion rate from 60 oC to 80 oC. Further increase in temperature up to 130 oC resulted in a decrease in corrosion rate. The existence of a maximum point in the curve was attributed to the competing effects of two factors which include increased diffusivity of oxygen which dominates at low temperature and decreasing solubility of oxygen and insulation dry-out which dominates at temperatures exceeding 80 oC. The new commercial inhibitor was observed to mitigate the corrosion rate at the temperatures investigated in this study. The inhibition efficiency indicated an average of 89% when a dosage of 5.2 g/m2 of the inhibitor was used. The effectiveness was also observed to be dosage dependent with lower doses having less inhibition efficiency. The drying times of the insulation assessed using galvanic current and impedance methods were observed to decrease as temperature increased. The galvanic current was observed to decrease to zero while the impedance increased to high values as the insulation dries out. However, the drying times obtained from galvanic current method showed a higher variability compared to impedance method. The result of prediction of CUI rate using Artificial Neural Network indicated an increase in accuracy as the number of input parameters increased. Surprisingly, the accuracy of the predicted output from the four input parameters (temperature, dosage of inhibitor, quantity of electrolyte in insulation and sample position) was higher than the accuracy of the most influential parameters (temperature and dosage of inhibitor). This suggests that incorporation of more input parameters having some relationship with the output is more important in achieving a higher accuracy compared to using the most influential parameters only. In conclusion, this study indicated that mineral wool insulation absorbs water for a long period without saturation which increases the risk of CUI. Also, CUI rate increased with temperature up to 80 oC but decreased on further increase up to 130 oC. The new commercial inhibitor was effective in mitigating CUI at the temperatures investigated. Also, more test solution was observed at the lower part of the insulation compared to the upper part when installed on the CUI test rig which increases the risk of severe corrosion at the lower section of the insulation. The prediction of CUI rate using ANN indicated that inclusion of more input parameters could improve prediction accuracy. Moreover, the choice of activation functions also has effect on the accuracy of the predicted output

Rate of Penetration Prediction Utilizing Hydromechanical Specific Energy

The prediction and the optimization of the rate of penetration (ROP), an important measure of drilling performance, have increasingly generated great interest. Several empirical techniques have been explored in the literature for the prediction and the optimization of ROP. In this study, four commonly used artificial intelligence (AI) algorithms are explored for the prediction of ROP based on the hydromechanical specific energy (HMSE) ROP model parameters. The AIs explored are the artificial neural network (ANN), extreme learning machine (ELM), support vector regression (SVR), and least-square support vector regression (LS-SVR). All the algorithms provided results with accuracy within acceptable range. The utilization of HMSE in selecting drilling variables for the prediction models provided an improved and consistent methodology of predicting ROP with drilling efficiency optimization objectives. This is valuable from an operational point of view, because it provides a reference point for measuring drilling efficiency and performance of the drilling process in terms of energy input and corresponding output in terms of ROP. The real-time drilling data utilized are must-haves, easily acquired, accessible, and controllable during drilling operations

Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint, more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become greener, and hence to act in a manner not required previously. While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance technologies, that would foresee potential failures, making production safer, lowering downtime, increasing productivity and diminishing maintenance costs. Many efforts were applied in order to define the most accurate and effective predictive methods, however data scarcity affects the speed and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection, using the open public data from Petrobras, that was developed by experts. For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM and GRU backbones, were implemented for multi-class classification of undesirable events on naturally flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on Genetic Algorithms as being the most advanced methods for such kind of tasks. The research concluded with the best performing algorithm with 2 stacked GRU and the following vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units 47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%. As the world faces many issues, one of which is the detrimental effect of heavy industries to the environment and as result adverse global climate change, this project is an attempt to contribute to the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it more efficient, transparent and sustainable