Predictive Maintenance Technologies Research Articles

Predictive Maintenance in Bridge Infrastructure

Predictive maintenance, driven by artificial intelligence and data analytics, represents a transformative approach to infrastructure management. By continuously monitoring asset conditions through sensors (Vibration, Temperature, crack, moisture and corrosion, 3D vision sensor and Ultrasonic & sonic) and advanced algorithms, predictive maintenance can forecast maintenance needs before infrastructure failures occur, leading to efficient scheduling, reduced costs, minimized downtime, and improved safety. Beyond cost savings, this approach enhances sustainability by optimizing resource usage, extending asset service life, and reducing environmental impact. Real-world applications in road infrastructure and the power sector demonstrate its potential to ensure reliability, prevent accidents, and meet the growing demands placed on critical infrastructure. Embracing predictive maintenance technologies is essential for addressing 21st-century infrastructure challenges effectively and safeguarding the well-being of communities

Transforming equipment management in oil and gas with AI-Driven predictive maintenance

The oil and gas industry faces significant challenges in managing equipment maintenance due to the complexity and criticality of its assets. Traditional maintenance approaches are often reactive and inefficient, leading to costly downtime and safety risks. However, the emergence of artificial intelligence (AI) and predictive maintenance technologies offers a transformative solution to these challenges. This paper explores the role of AI-driven predictive maintenance in revolutionizing equipment management in the oil and gas sector. AI-driven predictive maintenance leverages machine learning algorithms to analyze equipment data and predict when maintenance is required before a breakdown occurs. By monitoring equipment performance in real-time, AI can identify potential issues early, allowing operators to take proactive maintenance actions. This approach helps minimize downtime, reduce maintenance costs, and improve overall equipment reliability and safety. The implementation of AI-driven predictive maintenance requires a comprehensive strategy that includes data collection, analysis, and integration with existing maintenance practices. Successful adoption of AI-driven predictive maintenance can lead to significant benefits for oil and gas companies, including increased equipment uptime, extended asset lifespan, and enhanced operational efficiency. This paper reviews the current landscape of equipment management in the oil and gas industry, highlighting the limitations of traditional maintenance practices and the need for a more proactive approach. It then examines the principles and benefits of AI-driven predictive maintenance, showcasing real-world examples of its successful implementation. Finally, the paper discusses the challenges and considerations for implementing AI-driven predictive maintenance and provides recommendations for oil and gas companies looking to transform their equipment management practices. Keywords: Transforming Equipment; Management; Oil and Gas; AI-Driven; Predictive Maintenance.

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices.

Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies' flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors.

Efforts to Improve Ship Main Engine Efficiency and Ensure Its Safety

Efforts to improve ship main engine efficiency and ensure its safety have been ongoing endeavors in the maritime industry, driven by the dual imperatives of economic competitiveness and environmental sustainability. The main engine of a ship serves as its heart, propelling it through the vast expanses of the world's oceans while consuming significant quantities of fuel. Therefore, enhancing its efficiency directly impacts operational costs and reduces the environmental footprint associated with maritime transport. One primary focus of improving main engine efficiency lies in the advancement of engine design and technology. Over the years, there has been a concerted effort to develop engines that offer higher power output while consuming less fuel. This has led to the emergence of more efficient engine designs, such as slow-speed two-stroke engines and high-pressure, common-rail fuel injection systems. These innovations optimize fuel combustion, minimize energy losses, and enhance overall propulsion efficiency. Moreover, ongoing research and development efforts are aimed at harnessing alternative fuels and propulsion technologies to further enhance efficiency and reduce environmental impact. LNG (liquefied natural gas) has gained traction as a cleaner-burning fuel alternative, offering significant reductions in sulfur oxides (SOx) and nitrogen oxides (NOx) emissions compared to traditional marine fuels. Additionally, the exploration of hybrid and electric propulsion systems holds promise for reducing greenhouse gas emissions and improving overall energy efficiency. Ensuring the safety of ship main engines is paramount to the reliability and operability of vessels at sea. A robust maintenance regimen is essential to identify and address potential issues before they escalate into costly failures or pose safety hazards. Routine inspections, preventive maintenance, and condition monitoring play crucial roles in detecting abnormalities, wear, and tear, allowing for timely interventions and repairs. Furthermore, advancements in predictive maintenance technologies, such as remote monitoring systems and data analytics, enable real-time assessment of engine performance and early detection of potential malfunctions. The ARAS method lacks the capability to handle ambiguity, subjective judgments, and coping with incomplete information. It relies on unbiased good judgment to address uncertainty, particularly in unknown and complex conditions, making it a valuable approach. The method provides options for sequencing and analysis based on facts and special cases, allowing selectors to express both optimistic and rational attitudes. While it appears numerical on paper, it offers the flexibility to create e-learning pathways tailored to individual needs, emphasizing the importance of mastery. The proposed integrated software for this method is both cost-effective and validated for suitability, ensuring its practical application. Advanced Engine Designs, Improved Monitoring and Maintenance, Enhanced Fuel Management, Innovative Propulsion Technologies, Automation and Remote Monitoring and Advanced Materials and Coatings. the Rank Efforts to improve ship Main Engine efficiency and Ensure its safety for Additive Ratio Assessment method. Advanced Materials and Coatings is showing the highest rank and Advanced Engine Designs is showing the lowest rank. Fuel Efficiency Improvement, Emissions Reduction, Reliability and Maintenance Costs and Safety Performance.

IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks

Industry 4.0 technologies need to plan reactive and Preventive Maintenance (PM) strategies for their production lines. This applied research study aims to employ the Predictive Maintenance (PdM) technology with advanced automation technologies to counter all expected maintenance problems. Moreover, the deep learning based AI is employed to interpret the alarming patterns into real faults by which the system minimizes the human based fault recognition errors. The Sensors Information Modeling (SIM) and the Internet of Things (IoT) have the potential to improve the efficiency of industrial production machines maintenance management. This research work provides a better maintenance strategy by utilizing a data-driven predictive maintenance planning framework based on our proposed SIM and IoT technologies. To verify the feasibility of our approach, the proposed framework is applied practically on a corrugated cardboard production factory in real industrial environment. The Fuzzy Logic System (FLS) is utilized to achieve the AI based PM while the Deep Learning (DL) is applied for the alarming and fault diagnosis in case the fault already occured.

Investigation of Fault Diagnosis and Prognostics Techniques for Predictive Maintenance in Industrial Machinery

This research looks into fault detection and prognostic methods for industrial machinery predictive maintenance to maximize equipment dependability, reduce downtime, and improve operational effectiveness. The project aims to investigate integrated fault diagnosis and prognostics methodologies, analyze their applications in different industrial sectors, and determine policy implications to encourage implementation. Peer-reviewed articles, industry reports, case studies, and other current material are thoroughly reviewed as part of the technique. Major conclusions demonstrating the value of integrated fault diagnosis and prognostics in early fault identification, proactive decision-making, and optimal maintenance scheduling have been drawn from case studies in the power generating, petrochemical refining, and automotive manufacturing industries. The policy ramifications encompass the requirement for staff training, data standardization, investment in R&D, and regulatory frameworks to surmount constraints and stimulate innovation in industrial maintenance procedures. Organizations must adopt predictive maintenance technology to maintain competitiveness, cut expenses, and guarantee the dependable operation of vital mechanical assets in changing circumstances.

Transactional and Transformational Leadership Styles and Their Impact on Employees’ Acceptance of Predictive Maintenance Analytics: Evidence From an Indonesian Mining Company

This study aimed to investigate the role of leadership in the acceptance of predictive maintenance technologies in the coal mining industry. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used to assess the acceptance, and the Multifactor Leadership Questionnaire (MLQ) 5X was used to assess the perceived leadership styles of managers. The key findings of this study revealed that transactional leadership had a significant effect on performance expectancy, effort expectancy, and social influence, whereas transformational leadership did not significantly impact these aspects. Furthermore, an examination of the subdimensions of transformational and transactional leadership showed that contingent reward, management-by-exception (active), and intellectual stimulation had a considerable influence on the core constructs of the UTAUT model. These findings suggest that for adopting new technologies, the coal mining industry may require a different approach to exert leadership, and a comprehensive approach with consideration of the various subdimensions of leadership may be more effective in promoting acceptance and application of the technologies.

Research on Embedded Ball Screw Diagnosis System for Mechanical Fault Based on Bispectral Image

Ball screw is one of the main basic transmission components in CNC machine tool feed system because of its high transmission efficiency, positioning accuracy, and rigidity. It plays an irreplaceable role in the process of machine tool torque transmission and positioning. However, ball screw is also one of the parts which are easy to break down in the feed system. If it breaks down, it will affect the processing accuracy of the whole machine tool and even cause shutdown. Numerical control means that digital control is a high-efficiency technology that uses digital information to control mechanical movement and processing processes. Therefore, to increase the high-speed and high-precision operation ability of CNC machine tools, research and development of efficient and reliable predictive maintenance and issue identification technology to guide maintenance of CNC machine tools are critical. Throughout this study, a bispectrum image-based embedded defect fight against the disease for cylindrical gears is provided. Firstly, the bispectrum of fault signal is generated by Wavelet Packet Threshold denoising, wavelet packet reconstruction, and bispectrum analysis, and the characteristics of bispectrum are represented by the first kind of gray moments. These feature statistics are used as input feature vectors of BP neural network pattern recognition algorithm to classify these feature sets and identify the corresponding gear fault types. Experiments show that under the condition of noise, after 14.5 hours of testing, the NGI has increased significantly and can be used to diagnose faults in the gear system, and the method achieves relatively ideal recognition rate and verifies the feasibility of gear box fault diagnosis method based on image recognition. The code compiled by OpenCV library is easy to transplant to embedded system, with high development efficiency and reliable program operation. OpenCV has the characteristics of powerful matrix computing capability and convenient and flexible interface, and it is a general algorithm that can realize real-time image processing.

Analisis Predictive Maintenance Peralatan Lab Berbasis Machine Learning

The rapid development of AI is also supported by our entry into the digital era and the Internet of Things (IoT). In using laboratory equipment, students are required to comply with the rules so that the equipment can be maintained properly. However, the tool used will cause thirst for the tool. This becomes a problem when the tool is needed for learning, while the tool does not function properly or is completely damaged. The research method used in Predicting Labor Equipment of the Department of Electronics FT-UNP is the Cross-Industry Standard Process for Data Mining (CRISP-DM) development method. CRISP-DM is a very reliable model. By utilizing Predictive Maintenance technology on each existing equipment, we can analyze data to identify damage with failure mode, so as to obtain data regarding the frequency of occurrence of damage and the severity of damage to labor equipment and setting a warning for labor technicians, at this time there will be an alarm for technicians to perform checking of equipment.

FORMULA: A Deep Learning Approach for Rare Alarms Predictions in Industrial Equipment

Predictive Maintenance technologies are particularly appealing for Industrial Equipment producers, as they pave the way to the selling of high added-value services and customized maintenance plans. However, standard Predictive Maintenance approaches assume the availability of sensor measurements, and the costs associated with adding sensors or remotely accessing sensor readings may discourage the development of such technologies. In this context, Alarm Forecasting can be very useful as it represents a low-cost alternative or helpful support to sensor-based Predictive Maintenance. In this work, we propose a new formulation for the Alarm Forecasting problem, framed as a multi-label classification task. We present a novel deep learning-based approach called FORMULA (alarm FORecasting in MUlti-LAbel setting). FORMULA leverages Transformer, a popular Neural Network architecture in the field of Natural Language Processing. To cope with alarm imbalance, we draw inspiration from Segmentation and Object Detection. Thus, FORMULA is trained by minimizing the Weighted Focal Loss, which turns out to be very effective in predicting rare alarms. These alarms, even if they are difficult to predict by nature, often are business-critical. We assess the proposed approach on a representative real-world problem from the packaging industry. In particular, we show that it outperforms not only classic multilabel techniques but also models based on recurrent neural networks. As regards the latter, the proposed approach also exhibits a lower computational burden, both in terms of training time and model size. To foster research in the field and reproducibility, we also publicly share the alarm logs dataset and the code used to perform the experiments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the problem of monitoring equipment in the scenario of dairy products packaging, under the mild assumption that logs of the alarm generated by the packaging machines are available. This paper proposes an alarm forecasting algorithm. Its goal is to predict if any alarm will occur in the future, based only on past alarm logs. The limits of the considered future window can be defined arbitrarily, so there is enough time to perform corrective actions. Thus, the proposed approach aims to prevent unexpected downtime that would not only hinder productivity but also imply significant material waste. The proposed approach leverages methodologies from Natural Language Processing and Object Detection to deal with rare alarms that are often very informative in the industrial scenario. Besides, both the code and the real-world industrial datasets used to evaluate the methodology are available publicly. Currently, the proposed approach only uses alarm logs. Especially in the context of Industry 4.0, where many sensory data may be available, this is a limitation. Thus, the described approach might be extended by integrating alarm logs with sensory data. This integration is expected to improve the estimation of equipment health state. The results described in this paper may find application not only in the manufacturing sector but also in different areas such as Cyber Security, where log files keep activity records of each process performed.

A method for economic evaluation of predictive maintenance technologies by integrating system dynamics and evolutionary game modelling

Predictive maintenance technologies can be employed for failure prediction and system health management. Nevertheless, the additional cost involved in establishing the predictive maintenance system can be an obstacle to its widespread application. The decision on the predictive maintenance technology adoption can be made through the computation of the return on investment. To investigate the mechanisms of dynamic game between stakeholders involved in predictive maintenance, we establish the SD-EGT model from the perspective of systems engineering. This paper aims to propose an integrated method for the economic evaluation of predictive maintenance technologies by considering the incremental costs and benefits associated with its deployment. As an exemplary case, we take the Lithium-ion batteries whose failures have led to unexpected safety accidents. Firstly, we construct a quantitative relationship model between the failure modes and the predictive benefits of Lithium-ion battery systems to quantify the incremental benefits. Then, we establish a cost-benefit analysis (CBA) model by using system dynamics (SD) to make decisions about cost-effectiveness. Secondly, to optimize the cost investment strategy for the predictive maintenance technology, we develop an enterprise-government evolutionary game model, considering the information asymmetry between players. Eventually, we conduct a sensitivity analysis of the static subsidy strategy. The proposed methodology is serviceable to optimize the decision-making of predictive maintenance technology investment, which is a difficult yet very important task in industrial practice.

ESS 예측 유지보수를 위한 리튬이온 배터리 운용 결과 실증 데이터 분석

ESS is expanding with the spread of renewable energy, but fire and explosion accidents may occur due to fault. Therefore, predictive maintenance technology for efficiency and safety is becoming more important. In this paper, for predictive maintenance of lithium-ion batteries, empirical data for battery were analyzed and the time of fault was predicted using the cell voltage deviation.

A Study on the Predictive Maintenance Algorithms Considering Load Characteristics of PMSMs to Drive EGR Blowers for Smart Ships

Exhaust gas recirculation (EGR) is a NOx reduction technology that can meet stringent environmental regulatory requirements. EGR blower systems must be used to increase the exhaust gas pressure at a lower rate than the scavenging air pressure. Electric motor drive systems are essential to rotate the EGR blowers. For the effective management of the EGR blower systems in smart ships, there is a growing need for predictive maintenance technology fused with information and communication technology (ICT). Since an electric motor accounts for about 80% of electric loads driven by the EGR, it is essential to apply the predictive maintenance technology of the electric motor to maximize the reliability and operation time of the EGR blower system. Therefore, this paper presents the predictive maintenance algorithm to prevent the stator winding turn faults, which is the most significant cause of the electrical failure of the electric motors. The proposed algorithm predicts the remaining useful life (RUL) by obtaining the winding temperature value by considering the load characteristics of the electric motor. The validity of the proposed algorithm is verified through the simulation results of an EGR blower system model and the experimental results derived from using a test rig.

Programmable Motion-Fault Detection for a Collaborative Robot

Smart factories should be able to respond to catastrophic situations proactively, such as recalls caused by production line disruptions and equipment failures. Therefore, the necessity for predictive maintenance technology, such as fault detection or diagnosis of equipment has increased in recent years. In particular, predicting the faults of collaborative robots is becoming increasingly crucial because smart factories pursue efficient collaboration between humans and devices. However, collaborative robots have the characteristic of executing programmable motions designed by an operator, rather than performing fixed tasks. If existing fault diagnosis methods are applied to non–fixed programmable motions, problems arise in terms of setting absolute criteria for fault analysis, interpreting the meanings of detected values, and fault tracking or fault cause analysis. Therefore, we propose a method of programmable motion-fault detection by analyzing motion residuals to solve the three problems mentioned above. The proposed method can expand the fault diagnostic range of collaborative robots.

Research on Key Technology of Environmental and Efficient Coal Storage and Loading

In this paper, the existing problems of coal storage and loading technology are put forward, such as serious environmental pollution, low efficiency and low degree of intelligence. Aiming at these problems, the large capacity closed coal storage technology, intelligent collaborative control technology of storage and transportation equipment, intelligent unmanned rapid quantitative loading technology, intelligent unmanned vehicle dispatching technology, and remote operation and maintenance technology and predictive maintenance technology of storage and loading electromechanical equipment are systematically studied, and the corresponding solutions are proposed. It is of great significance to promote the development of environmental and efficient coal storage and loading technology.

First Strike Capability

Plastics EngineeringVolume 76, Issue 10 p. 44-47 Equipment Maintenance First Strike Capability Predictive maintenance technologies give processors and other manufacturers a way of preventing unplanned downtime Jim Romeo, Jim RomeoSearch for more papers by this author Jim Romeo, Jim RomeoSearch for more papers by this author First published: 10 December 2020 https://doi.org/10.1002/peng.20412Read the full textAboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume76, Issue10November/December 2020Pages 44-47 RelatedInformation

Time Series Prediction Algorithm for Intelligent Predictive Maintenance

Predictive maintenance aims to find out when the target device (TD) is in the sick state and almost entering the dead state before its actual occurrence to conduct just-in-time maintenance, so as to avoid unexpected TD down time. In this way, not only tool availability and manufacturing quality are improved, but the additional cost of excessive maintenance in preventive maintenance strategy can also be reduced. Among the predictive maintenance technologies proposed by many scholars, exponential model was commonly applied to predict the remaining useful life (RUL) of TD. However, due to the algorithm limitations, when TD is about to die, whether the TD's aging feature suddenly rises or becomes smooth, the exponential model may not be able to keep up with the real-time prediction or even falsely predicts long RUL. To solve the problem of inaccurate RUL prediction, the authors propose the time series prediction (TSP) algorithm. TSP applies the time series analysis model built by information criterion to adapt to the complicated future trend of solving TD fault prediction. Also, the Pre-Alarm Module (PreAM) to make alert of immediate maintenance when a TD is likely to shut down shortly as well as the Death Correlation Index (DCI) to reveal the possibility of entering the dead state are proposed in this work. How to select the most effective predictors and adjust the predictor weights to construct high-performance prediction model are also illustrated in this letter with the tools in various industries (such as solar-cell manufacturing and machine tool industry) being the examples of the TSP algorithm.

Health Monitoring of Centrifugal Pumps Using Digital Models

The area of predictive maintenance (PM) has received growing research interest in the past few years. Diagnostic capabilities of PM technologies have increased due to advances made in sensor technologies, signal processing algorithms, and the rapid development of computational power and data handling algorithms. Conventional PM programs are mostly built around analyzing sensors' data collected from physical systems. Incorporating simulation data collected from digital models replicating the physical system with sensors' data can lead to more optimization for operation and maintenance. This paper demonstrates the role of using digital models in implementing effective condition monitoring on centrifugal pumps. Two digital models are used to study the dynamic performance of a centrifugal pump experiencing cavitation condition. The first model is a three-dimensional fully turbulent computational fluid dynamic (CFD) model. Based on the pressure distribution obtained from the CFD, a novel analytical pressure pulsation model is developed and used to simulate the exciting forces affecting the pump. The second digital model is a pump casing dynamic model which is used to predict the casing vibration response to exciting forces due to faulty operating conditions. Results obtained from the digital models are validated using an experimental test rig of a small centrifugal pump. Using this concept, a pump faulty operation can be simulated to provide complete understanding of the root cause of the fault. Additionally, digital models can be used to simulate different corrective actions that would restore the normal operation of the pump.

A hidden-Gamma model-based filtering and prediction approach for monotonic health factors in manufacturing

In the context of Smart Monitoring and Fault Detection and Isolation in industrial systems, the aim of Predictive Maintenance technologies is to predict the happening of process or equipment faults. In order for a Predictive Maintenance technology to be effective, its predictions have to be both accurate and timely for taking strategic decisions on maintenance scheduling, in a cost-minimization perspective. A number of Predictive Maintenance technologies are based on the use of “health factors”, quantitative indicators associated with the equipment wear that exhibit a monotone evolution. In real industrial environment, such indicators are usually affected by measurement noise and non-uniform sampling time. In this work we present a methodology, formulated as a stochastic filtering problem, to optimally predict the evolution of the aforementioned health factors based on noisy and irregularly sampled observations. In particular, a hidden Gamma process model is proposed to capture the nonnegativity and nonnegativity of the derivative of the health factor. As such filtering problem is not amenable to a closed form solution, a numerical Monte Carlo approach based on particle filtering is here employed. An adaptive parameter identification procedure is proposed to achieve the best trade-off between promptness and low noise sensitivity. Furthermore, a methodology to identify the risk function associated to the observed equipment based on previous maintenance data is proposed. The present study is motivated and tested on a real industrial Predictive Maintenance problem in semiconductor manufacturing, with reference to a dry etching equipment.

Possible ways to achieving high-accuracy sizing of defects discovered by ILI tools

THE MAIN GOAL of in-line inspection (ILI) is (according to the seven basic ILI quality metrics [1]) to correctly detect, locate, and identify all types of defects present in a pipeline and to size them in a fashion which allows statistical assessment of their true sizes. If achieved, the last fact opens the door widely to meaningful usage of the most sophisticated methods of structural mechanics (which were developed spending worldwide billions of $$$ but not used yet to its fullest in the pipeline industry) and obtaining most-accurate values possible of pipeline residual strength, probability of failure, and residual lifetime. This, in its turn, permits using predictive-maintenance technology in pipeline operation, introducing optimal inspections and repair logistics, and maximizing the long-term utility of the asset (in our case, the pipeline system). The paper describes the methodology developed by the authors of a holistic innovative approach to ILI data generation and data management, which dramatically increases ILI inspection capabilities. This methodology, to a large extent, decreases the existing uncertainties and minimizes scatter of the input parameters and, thereby, makes predictions based on ILI data less conservative. As a result, this permits creation of safe solutions and avoidance of dangerous errors in predictions which include assessment of pipeline inspection frequency and safety margins. According to the API 1163 Standard [2], the ILI measurement results are characterized by three parameters of statistical nature: tolerance, certainty, and the confidence level. Tolerance is the range with which an anomaly dimension or characteristic is sized or characterized, and certainty is the probability that a reported anomaly characteristic is within a stated tolerance. Confidence level is a statistical term used to describe the mathematical certainty with which a statement is made, and indicates the confidence with which the tolerance and certainty levels are satisfied. The paper discusses the statistical sources of these probabilities and how they should be interpreted and handled. The paper contains recommendations on how to approach different practical problems, and illustrates each case with real-life examples.