Optimizing Rotating Equipment Maintenance Through Machine Learning Algorithm

2021 ◽  
Author(s):  
Francesco Beduschi ◽  
Fabio Turconi ◽  
Basso De Gregorio ◽  
Francesca Abbruzzese ◽  
Annagiulia Tiozzo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Failure Modes ◽  
Learning Algorithm ◽  
Principal Component ◽  
Fault Isolation ◽  
Operating Conditions ◽  
Equipment Maintenance ◽  
Predictive Tool ◽  
Rotating Equipment

Abstract This work highlights the development and results of a Rotating equipment predictive maintenance tool that allows to monitor the status of rotating machines through a synthetic "health index" and early detection of anomalies. The data-driven proposed solution is of great help to maintenance engineers, who, alongside the existing methodologies, can apply an effective tool based on artificial intelligence for early prevention of failures. Taking advantage of the high availability of remote sensors data, an anomaly detection machine learning model, which relies on Principal Component Analysis (PCA) and Kernel Density Estimation (KDE), has been built. This model is capable of estimating, in real time, the health status of the machine, by matching the sensors actual values with the reference ones based on the Normal Operating Conditions (NOC) periods, that have been previously identified. If an anomalous behavior is detected, the Fault Isolation step of the model allows to evaluate which are the most contributing sensors for the investigated anomaly. These outcomes, combined with a failure mode matrix, which links the sensors deviations with the possible malfunctions, allows to highlight the most likely failure modes to be associated to the investigated anomaly. The developed predictive tool has been implemented on operating sites and it has demonstrated the capability to generate accurate warnings and detect anomalies to be processed by the maintenance engineers. These alerts may be aggregated into events in order to be monitored and analyzed by remote and on site specialists. The availability of alerts gives to the users the possibility to predict any deterioration of the machines or process fluctuations, that could lead to unplanned events with consequent mechanical breakdowns, production losses and flaring events. As a consequence, tailored operative adjustment to prevent critical events can be taken. Thanks to the tool, it is also possible to monitor over time the equipment behavior in order to provide suggestions for maintenance plans optimization and other useful statistics concerning the most recurrent failure. The tool's innovative feature is the ability to utilize the giant amount of data and to reproduce complex field phenomena by means of artificial intelligence. The proposed tool represents an innovative predictive approach for rotating equipment maintenance optimization.

Prediction of Threshold Sand Rates from Acoustic Monitors Using Artificial Intelligence

2021 ◽  
Author(s):  
Ronald E. Vieira ◽  
Bohan Xu ◽  
Asad Nadeem ◽  
Ahmed Nadeem ◽  
Siamack A. Shirazi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Noise Level ◽  
Background Noise ◽  
Pipe Wall ◽  
Flow Patterns ◽  
Operating Conditions ◽  
Flow Conditions ◽  
Background Noise Level ◽  
Input Parameters

Abstract Solids production from oil and gas wells can cause excessive damage resulting in safety hazards and expensive repairs. To prevent the problems associated with sand influx, ultrasonic devices can be used to provide a warning when sand is being produced in pipelines. One of the most used methods for sand detection is utilizing commercially available acoustic sand monitors that clamp to the outside of pipe wall and measures the acoustic energy generated by sand grain impacts on the inner side of a pipe wall. Although the transducer used by acoustic monitors is especially sensitive to acoustic emissions due to particle impact, it also reacts to flow induced noise as well (background noise). The acoustic monitor output does not exceed the background noise level until a sufficient sand rate is entrained in the flow that causes a signal output that is higher than the background noise level. This sand rate is referred to as the threshold sand rate or TSR. A significant amount of data has been compiled over the years for TSR at the Tulsa University Sand Management Projects (TUSMP) for various flow conditions with stainless steel pipe material. However, to use this data to develop a model for different flow patterns, fluid properties, pipe, and sand sizes is challenging. The purpose of this work is to develop an artificial intelligence (AI) methodology using machine learning (ML) models to determine TSR for a broad range of operating conditions. More than 250 cases from previous literature as well as ongoing research have been used to train and test the ML models. The data utilized in this work has been generated mostly in a large-scale multiphase flow loop for sand sizes ranging from 25 to 300 μm varying sand concentrations and pipe diameters from 25.4 mm to 101.6 mm ID in vertical and horizontal directions downstream of elbows. The ML algorithms including elastic net, random forest, support vector machine and gradient boosting, are optimized using nested cross-validation and the model performance is evaluated by R-squared score. The machine learning models were used to predict TSR for various velocity combinations under different flow patterns with sand. The sensitivity to changes of input parameters on predicted TSR was also investigated. The method for TSR prediction based on ML algorithms trained on lab data is also validated on actual field conditions available in the literature. The AI method results reveal a good training performance and prediction for a variety of flow conditions and pipe sizes not tested before. This work provides a framework describing a novel methodology with an expanded database to utilize Artificial Intelligence to correlate the TSR with the most common production input parameters.

Innovative Approach to Build a Scalable Hybrid Model for Prescriptive Maintenance

2021 ◽  
Author(s):  
Richard Büssow ◽  
Bruno Hain ◽  
Ismael Al Nuaimi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Hybrid Model ◽  
Domain Knowledge ◽  
Spare Parts ◽  
Good Prediction ◽  
First Principle ◽  
Plant Data ◽  
Rotating Equipment

Abstract Objective and Scope Analysis of operational plant data needs experts in order to interpret detected anomalies which are defined as unusual operation points. The next step on the digital transformation journey is to provide actionable insights into the data. Prescriptive Maintenance defines in advance which kind of detailed maintenance and spare parts will be required. This paper details requirements to improve these predictions for rotating equipment and show potential to integrate the outcome into an operational workflow. Methods, Procedures, Process First principle or physics-based modelling provides additional insights into the data, since the results are directly interpretable. However, such approaches are typically assumed to be expensive to build and not scalable. Identification of and focus on the relevant equipment to be modeled in a hybrid model using a combination of first principle physics and machine learning is a successful strategy. The model is trained using a machine learning approach with historic or current real plant data, to predict conditions which have not occurred before. The better the Artificial Intelligence is trained, the better the prediction will be. Results, Observations, Conclusions The general aim when operating a plant is the actual usage of operational data for process and maintenance optimization by advanced analytics. Typically a data-driven central oversight function supports operations and maintenance staff. A major lesson-learned is that the results of a rather simple statistical approach to detect anomalies fall behind the expectations and are too labor intensive. It is a widely spread misinterpretation that being able to deal with big data is sufficient to come up with good prediction quality for Prescriptive Maintenance. What big data companies are normally missing is domain knowledge, especially on plant critical rotating equipment. Without having domain knowledge the relevant input into the model will have shortcomings and hence the same will apply to its predictions. This paper gives an example of a refinery where the described hybrid model has been used. Novel and Additive Information First principle models are typically expensive to build and not scalable. This hybrid model approach, combining first principle physics based models with artificial intelligence and integration into an operational workflow shows a new way forward.

scholarly journals Research on machine learning Algorithm optimization based on 0-1 coding

2021 ◽  
Vol 2083 (4) ◽  
pp. 042086
Author(s):  
Yuqi Qin
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Manufacturing Industry ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Algorithm Optimization ◽  
The Core ◽  
Coding Method ◽  
Discrete Manufacturing

Abstract Machine learning algorithm is the core of artificial intelligence, is the fundamental way to make computer intelligent, its application in all fields of artificial intelligence. Aiming at the problems of the existing algorithms in the discrete manufacturing industry, this paper proposes a new 0-1 coding method to optimize the learning algorithm, and finally proposes a learning algorithm of “IG type learning only from the best”.

Hybridization of Machine Learning Algorithm in Intrusion Detection System

2020 ◽  
pp. 150-175
Author(s):  
Amudha P. ◽  
Sivakumari S.
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Classification Accuracy ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Feature Selection Technique ◽  
Efficient Manner

In recent years, the field of machine learning grows very fast both on the development of techniques and its application in intrusion detection. The computational complexity of the machine learning algorithms increases rapidly as the number of features in the datasets increases. By choosing the significant features, the number of features in the dataset can be reduced, which is critical to progress the classification accuracy and speed of algorithms. Also, achieving high accuracy and detection rate and lowering false alarm rates are the major challenges in designing an intrusion detection system. The major motivation of this work is to address these issues by hybridizing machine learning and swarm intelligence algorithms for enhancing the performance of intrusion detection system. It also emphasizes applying principal component analysis as feature selection technique on intrusion detection dataset for identifying the most suitable feature subsets which may provide high-quality results in a fast and efficient manner.

Introduction to Machine Learning and Its Application

2020 ◽  
pp. 262-290
Author(s):  
Ladly Patel ◽  
Kumar Abhishek Gaurav
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Machine Learning Algorithm ◽  
Past Data ◽  
Applications Of Machine Learning ◽  
Learning Frameworks ◽  
Learning Data

In today's world, a huge amount of data is available. So, all the available data are analyzed to get information, and later this data is used to train the machine learning algorithm. Machine learning is a subpart of artificial intelligence where machines are given training with data and the machine predicts the results. Machine learning is being used in healthcare, image processing, marketing, etc. The aim of machine learning is to reduce the work of the programmer by doing complex coding and decreasing human interaction with systems. The machine learns itself from past data and then predict the desired output. This chapter describes machine learning in brief with different machine learning algorithms with examples and about machine learning frameworks such as tensor flow and Keras. The limitations of machine learning and various applications of machine learning are discussed. This chapter also describes how to identify features in machine learning data.

scholarly journals Artificial Intelligence (AI) to the Rescue: Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays

2020 ◽  
pp. 247263032096271
Author(s):  
Sunday Olakunle Idowu ◽  
Amos Akintayo Fatokun
Keyword(s):  
Oxidative Stress ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Antioxidant Capacity ◽  
Unmet Need ◽  
Hydrogen Atom Transfer ◽  
Support Vector ◽  
Antioxidant Action ◽  
Predictive Tool

Oxidative stress induced by excessive levels of reactive oxygen species (ROS) underlies several diseases. Therapeutic strategies to combat oxidative damage are, therefore, a subject of intense scientific investigation to prevent and treat such diseases, with the use of phytochemical antioxidants, especially polyphenols, being a major part. Polyphenols, however, exhibit structural diversity that determines different mechanisms of antioxidant action, such as hydrogen atom transfer (HAT) and single-electron transfer (SET). They also suffer from inadequate in vivo bioavailability, with their antioxidant bioactivity governed by permeability, gut-wall and first-pass metabolism, and HAT-based ROS trapping. Unfortunately, no current antioxidant assay captures these multiple dimensions to be sufficiently “biorelevant,” because the assays tend to be unidimensional, whereas biorelevance requires integration of several inputs. Finding a method to reliably evaluate the antioxidant capacity of these phytochemicals, therefore, remains an unmet need. To address this deficiency, we propose using artificial intelligence (AI)-based machine learning (ML) to relate a polyphenol’s antioxidant action as the output variable to molecular descriptors (factors governing in vivo antioxidant activity) as input variables, in the context of a biomarker selectively produced by lipid peroxidation (a consequence of oxidative stress), for example F2-isoprostanes. Support vector machines, artificial neural networks, and Bayesian probabilistic learning are some key algorithms that could be deployed. Such a model will represent a robust predictive tool in assessing biorelevant antioxidant capacity of polyphenols, and thus facilitate the identification or design of antioxidant molecules. The approach will also help to fulfill the principles of the 3Rs (replacement, reduction, and refinement) in using animals in biomedical research.

scholarly journals Implementation of modified SARSA learning technique in EMCAP

2017 ◽  
Vol 7 (1.5) ◽  
pp. 274
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Learning System ◽  
State Action ◽  
Learning Technique ◽  
Markov Decision ◽  
Experiment Analysis

This research work presents analysis of Modified Sarsa learning algorithm. Modified Sarsa algorithm.  State-Action-Reward-State-Action (SARSA) is an technique for learning a Markov decision process (MDP) strategy, used in for reinforcement learning int the field of artificial intelligence (AI) and machine learning (ML). The Modified SARSA Algorithm makes better actions to get better rewards.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance. This modified   SARSA learning algorithm can   be more suitable in EMCAP architecture.  The experiments are conducted the modified   SARSA Learning system gets   more rewards compare to existing  SARSA algorithm.

Contactless Li-Ion Battery Voltage Detection by Using Walabot and Machine Learning

2019 ◽  
Author(s):  
Yanan Wang ◽  
Haoyu Niu ◽  
Tiebiao Zhao ◽  
Xiaozhong Liao ◽  
Lei Dong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gradient Descent ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Lithium Ion ◽  
Principal Component ◽  
Stochastic Gradient Descent ◽  
Li Ion Battery ◽  
Linear Discriminant ◽  
Li Ion

Abstract This paper has proposed a contactless voltage classification method for Lithium-ion batteries (LIBs). With a three-dimensional radio-frequency based sensor called Walabot, voltage data of LIBs can be collected in a contactless way. Then three machine learning algorithm, that is, principal component analysis (PCA), linear discriminant analysis (LDA), and stochastic gradient descent (SGD) classifiers, have been employed for data processing. Experiments and comparison have been conducted to verify the proposed method. The colormaps of results and prediction accuracy show that LDA may be most suitable for LIBs voltage classification.

scholarly journals Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine

2020 ◽  
Vol 41 (7) ◽  
pp. 826-830 ◽  
Author(s):  
Arni S. R. Srinivasa Rao ◽  
Jose A. Vazquez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Phone ◽  
Learning Algorithm ◽  
Web Survey ◽  
Machine Learning Algorithm ◽  
Case Identification ◽  
Susceptible Populations

AbstractWe propose the use of a machine learning algorithm to improve possible COVID-19 case identification more quickly using a mobile phone–based web survey. This method could reduce the spread of the virus in susceptible populations under quarantine.

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