A Study on Comparison of Classification Algorithms for Pump Failure Prediction

The reliability of pumps can be compromised by faults, impacting their functionality. Detecting these faults is crucial, and many studies have utilized motor current signals for this purpose. However, as pumps are rotational equipped, vibrations also play a vital role in fault identification. Rising pump failures have led to increased maintenance costs and unavailability, emphasizing the need for cost-effective and dependable machinery operation. This study addresses the imperative challenge of defect classification through the lens of predictive modeling. With a problem statement centered on achieving accurate and efficient identification of defects, this study’s objective is to evaluate the performance of five distinct algorithms: Fine Decision Tree, Medium Decision Tree, Bagged Trees (Ensemble), RUS-Boosted Trees, and Boosted Trees. Leveraging a comprehensive dataset, the study meticulously trained and tested each model, analyzing training accuracy, test accuracy, and Area Under the Curve (AUC) metrics. The results showcase the supremacy of the Fine Decision Tree (91.2% training accuracy, 74% test accuracy, AUC 0.80), the robustness of the Ensemble approach (Bagged Trees with 94.9% training accuracy, 99.9% test accuracy, and AUC 1.00), and the competitiveness of Boosted Trees (89.4% training accuracy, 72.2% test accuracy, AUC 0.79) in defect classification. Notably, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and k-Nearest Neighbors (KNN) exhibited comparatively lower performance. Our study contributes valuable insights into the efficacy of these algorithms, guiding practitioners toward optimal model selection for defect classification scenarios. This research lays a foundation for enhanced decision-making in quality control and predictive maintenance, fostering advancements in the realm of defect prediction and classification

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

Profitability, reliability and condition based monitoring of LNG floating platforms: a review

The efficiency and profitability of Floating, Production, Storage and Offloading platform (FPSO) terminals depends on various factors such as LNG liquefaction process type, system reliability and maintenance approach. This review is organized along the following research questions: (i) what are the economic benefit of FPSO and how does the liquefaction process type affect its profitability profile?, (ii) how to improve the reliability of the liquefaction system as key section? and finally (iii) what are the major CBM techniques applied on FPSO. The paper concluded the literature and identified the research shortcomings in order to improve profitability, efficiency and availability of FPSOs

Maintenance management of tractors and agricultural machinery: Preventive maintenance systems

Agricultural machinery maintenance has a crucial role for successful agricultural production.  It aims at guaranteeing the safety of operations and availability of machines and related equipment for cultivation operation.  Moreover, it is one major cost for agriculture operations.  Thus, the increased competition in agricultural production demands maintenance improvement, aiming at the reduction of maintenance expenditures while keeping the safety of operations.  This issue is addressed by the methodology presented in this paper.  So, the aim of this paper was to give brief introduction to various preventive maintenance systems specially condition-based maintenance (CBM) techniques, selection of condition monitoring techniques and understanding of condition monitoring (CM) intervals, advancement in CBM, standardization of CBM system, CBM approach on agricultural machinery, advantages and disadvantages of CBM.  The first step of the methodology consists of concept condition monitoring approach for the equipment preventive maintenance; its purpose is the identification of state-of-the-art in the CM of agricultural machinery, describing the different maintenance strategies, CM techniques and methods.  The second step builds the signal processing procedure for extracting information relevant to targeted failure modes.   Keywords: agricultural machinery, fault detection, fault diagnosis, signal processing, maintenance managemen

DEVELOPMENT OF FORECASTING AND SCHEDULING METHODS AND DATA ANALYTICS BASED CONTROLS FOR SMART LOADS IN RESIDENTIAL BUILDINGS.

DEVELOPMENT OF FORECASTING AND SCHEDULING METHODS AND DATA ANALYTICS BASED CONTROLS FOR SMART LOADS IN RESIDENTIAL BUILDINGS

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

Jätevedenpuhdistamojen prosessinohjauksen ja operoinnin kehittäminen data-analytiikan avulla: esimerkkejä teollisuudesta ja kansainvälisiltä puhdistamoilta

Instrumentation, control and automation are central for operation of municipal wastewater treatment plants. Treatment performance can be further improved and secured by processing and analyzing the collected process and equipment data. New challenges from resource efficiency, climate change and aging infrastructure increase the demand for understanding and controlling plant-wide interactions. This study aims to review what needs, barriers, incentives and opportunities Finnish wastewater treatment plants have for developing current process control and operation systems with data analytics. The study is conducted through interviews, thematic analysis and case studies of real-life applications in process industries and international utilities. Results indicate that for many utilities, additional measures for quality assurance of instruments, equipment and controllers are necessary before advanced control strategies can be applied. Readily available data could be used to improve the operational reliability of the process. 14 case studies of advanced data processing, analysis and visualization methods used in Finnish and international wastewater treatment plants as well as Finnish process industries are reviewed. Examples include process optimization and quality assurance solutions that have proven benefits in operational use. Applicability of these solutions for identified development needs is initially evaluated. Some of the examples are estimated to have direct potential for application in Finnish WWTPs. For other case studies, further piloting or research efforts to assess the feasibility and cost-benefits for WWTPs are suggested. As plant operation becomes more centralized and outsourced in the future, need for applying data analytics is expected to increase.Prosessinohjaus- ja automaatiojärjestelmillä on keskeinen rooli modernien jätevedenpuhdistamojen operoinnissa. Prosessi- ja laitetietoa paremmin hyödyntämällä prosessia voidaan ohjata entistä tehokkaammin ja luotettavammin. Kiertotalous, ilmastonmuutos ja infrastruktuurin ikääntyminen korostavat entisestään tarvetta ymmärtää ja ohjata myös eri osaprosessien välisiä vuorovaikutuksia. Tässä työssä tarkastellaan tarpeita, esteitä, kannustimia ja mahdollisuuksia kehittää jätevedenpuhdistamojen ohjausta ja operointia data-analytiikan avulla. Eri sidosryhmien näkemyksiä kartoitetaan haastatteluilla, joiden tuloksia käsitellään temaattisen analyysin kautta. Löydösten perusteella potentiaalisia ratkaisuja kartoitetaan suomalaisten ja kansainvälisten puhdistamojen sekä prosessiteollisuuden jo käyttämistä sovelluksista. Löydökset osoittavat, että monilla puhdistamoilla tarvitaan nykyistä merkittävästi kattavampia menetelmiä instrumentoinnin, laitteiston ja ohjauksen laadunvarmistukseen, ennen kuin edistyneempien prosessinohjausmenetelmien käyttöönotto on mahdollista. Operoinnin toimintavarmuutta ja luotettavuutta voitaisiin kehittää monin tavoin hyödyntämällä jo kerättyä prosessi- ja laitetietoa. Työssä esitellään yhteensä 14 esimerkkiä puhdistamoilla ja prosessiteollisuudessa käytössä olevista prosessinohjaus- ja laadunvarmistusmenetelmistä. Osalla ratkaisuista arvioidaan sellaisenaan olevan laajaa sovelluspotentiaalia suomalaisilla jätevedenpuhdistamoilla. Useiden ratkaisujen käyttöönottoa voitaisiin edistää pilotoinnilla tai jatkotutkimuksella potentiaalisten hyötyjen ja kustannusten arvioimiseksi. Jo kerättyä prosessi- ja laitetietoa hyödyntävien ratkaisujen kysynnän odotetaan tulevaisuudessa lisääntyvän, kun puhdistamojen operointi keskittyy ja paineet kustannus- ja energiatehokkuudelle kasvavat

COMBINED DEEP AND SHALLOW KNOWLEDGE IN A UNIFIED MODEL FOR DIAGNOSIS BY ABDUCTION

Fault Diagnosis in real systems usually involves human expert’s shallow knowledge (as pattern causes-effects) but also deep knowledge (as structural / functional modularization and models on behavior). The paper proposes a unified approach on diagnosis by abduction based on plausibility and relevance criteria multiple applied, in a connectionist implementation. Then, it focuses elicitation of deep knowledge on target conductive flow systems – most encountered in industry and not only, in the aim of fault diagnosis. Finally, the paper gives hints on design and building of diagnosis system by abduction, embedding deep and shallow knowledge (according to case) and performing hierarchical fault isolation, along with a case study on a hydraulic installation in a rolling mill plant.shallow knowledge, diagnosis, flow systems

Wind turbine condition monitoring strategy through multiway PCA and multivariate inference

This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide range of significance, a in [1%, 13%], the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.Peer ReviewedPostprint (published version