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Pigging inspection tool systems are essential for maintaining the integrity and efficiency of pipelines. These systems involve the use of "pigs," which are devices that travel through the pipeline to perform various inspection and maintenance tasks. Here’s an overview of key aspects: Key Features Types of Pigs: Cleaning Pigs: Remove debris, scale, and other obstructions. Inspection Pigs (Smart Pigs): Equipped with sensors to gather data on pipeline conditions (e.g., wall thickness, corrosion). Seal Pigs: Used to isolate sections of a pipeline. Advanced Sensors: Many inspection pigs are equipped with sophisticated sensors, including ultrasonic, magnetic flux leakage (MFL), and inertial measurement units (IMUs) to assess pipeline integrity. Data Collection and Analysis: The tools gather data as they move through the pipeline, which can be analyzed to identify potential issues and determine maintenance needs. Real-Time Monitoring: Some systems allow for real-time data transmission, providing immediate insights into pipeline conditions. 𝐒𝐄𝐍𝐃 𝐈𝐍𝐐𝐔𝐈𝐑𝐘 𝐅𝐎𝐑 𝐐𝐔𝐎𝐓𝐄 𝐌𝐨𝐛𝐢𝐥𝐞 /𝐖𝐡𝐚𝐭𝐬𝐀𝐩𝐩+𝟒𝟒 𝟕𝟕𝟎𝟒 𝟔𝟓𝟓𝟔𝟓𝟔   𝐄𝐦𝐚𝐢𝐥: 𝐒𝐚𝐥𝐞𝐬@𝐞𝐥𝐭𝐢𝐞𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐩𝐢𝐠𝐬.𝐜𝐨𝐦  𝐖𝐞𝐛:  𝐰𝐰𝐰.𝐞𝐥𝐭𝐢𝐞𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐩𝐢𝐠𝐬.𝐜𝐨𝐦 

Given what’s at stake, it is essential that asset owners have total confidence in storage tank floor inspection findings and the ensuing reports delivered by inspection companies. Tank floor inspection equipment must not only identify material loss caused by corrosion, for example, but also the origins of this loss and it must do so in the most accurate, reliable, and efficient manner. Clearly, a critical criterion for any storage tank floor inspection is the classification of an indication origin – is the indication top surface (product side), bottom surface (soil side), or both? Knowledge of indication surface origin is crucial for formulating the most accurate and reliable maintenance and repair strategies, on which the health of the world depends. In this article, we look at the technology responsible for delivering clear data for better decision making. https://lnkd.in/dAsg-iJi

Bearings Health Monitoring Based on Frequency-Domain Vibration Signals Analysis https://lnkd.in/dTzt9aRi Abstract Rotating machine health monitoring is critical for system safety, cost savings, and increased reliability. The need for a simple and accurate fault diagnosis method has led to the development of various monitoring techniques. They incorporate vibration, motor’s current signature, and acoustic emission signals analysis in condition monitoring. So, based on using vibration signal analysis, a test rig was built for bearing fault identification. The test rig replicates and investigates various bearing problems, such as those found in the inner and outer races. An accelerometer, type ADXL335, was interfaced to a data acquisition device (DAQ USB-6215) for collecting vibration signals under various operating circumstances. In addition, a load cell was embedded with the test rig, interfaced with a digital panel meter, and used for recording the applied load on the bearings. The time-domain signal analysis technique was used after acquiring vibration signals at various bearing health states. Then, the time-domain signal was converted to the frequency domain using the fast Fourier transform, and the result was analyzed to investigate the generated fault frequencies. Finally, the obtained frequencies were compared with the theoretical values extracted from the theoretical equations, and the method proved its effectiveness in detecting the fault generated. Highlights: • Fabricate test rig to simulate the state and capture information • Extraction time domain signal. • Transform time domain to frequency domain by FFT transform using sigview program. • Analysis result. Keywords: • Vibration Signal Analysis • Bearing fault detection • Time-domain signal analysis • Frequency-domain signal analysis • Fault frequencies Journal: https://lnkd.in/dgnvtdte Issue: https://lnkd.in/d9W-V9tn Article: https://lnkd.in/e4PDye4C ETJ LinkedIn: https://lnkd.in/d_8SPqAt #Engineering_and_Technology_Journal #UOT #engineering #technology #etj

Crack Detection in Action: Tackling Environmental & Operational Variability and Mitigating Risk 🏢✨ What is Structural Health monitoring? Implementing automated and online damage detection assessment capabilities for any engineered systems (aerospace, mechanical and civil). The technique involves monitoring the system over time using periodically sampled dynamic responses via a group of sensors. The recorded data delivers an advantage over economic and life safety benefits. Simplifying Structural Health Monitoring (SHM): A Four-Step Paradigm - Operational evaluation: Determine the type of damage the SHM system will detect, consider economic and safety aspects, evaluate operational environmental conditions, and identify limitations on data acquisition under these conditions. - Data Acquisition: Detail the system excitation, sensing, data transmission, and storage processes. - Feature Extraction: Transform stored data into valuable information. Features are extracted using signal processing and statistics to characterize the measured response. - Statistical model development: Gain insights into the damaged state of the structure, including location, extent, type, and remaining life, based on the developed model.   In the proposed SHM system for TSMC, the aim is to detect pipeline cracks ranging from 5 to 10 cm, despite limited knowledge of the pipe geometry within the manufacturing unit. By implementing SHM on the major pipeline system, we can swiftly identify defects following seismic events and prevent significant cracks by detecting early-stage defects. The possible environmental and operational variabilities in the manufacturing unit will be varying temperatures and vibrations (noise source) coming from the vicinity of the machinery. Semiconductor manufacturing involves the use of expensive machinery and the operation of critical pipelines that carry hazardous fluids. Failure to detect leaks can lead to both human and machine casualties. Implementing SHM in this environment presents challenges, including the high cost of a denser sensor network required for accurate real-time crack detection. Additionally, mechanical vibrations and electrical and magnetic interference from heavy machinery introduce noise into the data. This noise must be cleaned and processed, potentially delaying results. Despite these challenges, the importance of effective SHM systems in ensuring safety and operational efficiency in semiconductor manufacturing cannot be overstated. #StructuralHealthMonitoring #SemiconductorIndustry #SafetyFirst #InnovativeTechnology #RiskMitigation

🔌 Advanced Generator Protection with PowerFactory PowerFactory is renowned for providing sophisticated tools for detailed simulation and analysis of protection systems, including pole slip incidents. Its capabilities enable engineers to ensure that power systems are not only stable and efficient but also robustly equipped to handle faults and disturbances. Highlighting PowerFactory's Visualization Capabilities: PowerFactory allows engineers to create detailed R-X plots that illustrate the relay characteristics and the generator's fault trajectory during pole slip conditions. This visualization aids in understanding how the relay responds to different fault dynamics and various pre-fault conditions, such as generator active and reactive power dispatch and system strength. These insights are crucial for optimising protection settings. Example of Pole Slip Protection: Attached to this post, you’ll find an R-X plot from a PowerFactory simulation. This plot shows the relay protection characteristic alongside the generator's fault trajectory, clearly marking the points where the protection relay triggers to isolate the fault. It effectively demonstrates the tripping time and the relay’s capability to protect the generator from damaging pole slip conditions. Optimising Protection Settings: Using insights from R-X plots, engineers can adjust protection settings to ensure timely responses to faults, minimizing potential damage and outages. PowerFactory’s tools facilitate precise coordination of these settings across complex network configurations, enhancing overall system stability. Why Choose PowerFactory? PowerFactory offers an integrated environment for simulating and optimising generator protection through advanced analytical tools. It's the go-to solution for engineers looking to elevate their protection strategies to the highest standard. #ElectricalEngineering #PowerSystems #PowerFactory #GeneratorProtection

Why Choose Acoustic Resonance Technology (ART) ? At NDT Global, we are constantly pushing the boundaries of pipeline inspection technology. Our Ultra-Wideband Acoustic Measurement Technology, ART Scan offers operators a robust, flexible, and accurate pipeline inspection for previously challenging lines. Using acoustic resonance technology, ART Scan provides sub-millimeter accuracy wall thickness measurements in both gas and liquid pipelines. Beyond wall thickness, the non-contact sensors also provide a full ultrasonic geometry survey of dents, buckles, out-of-straightness, and ovality. 💡 Why ART? ➡️ High Sensitivity: Identify even the smallest flaws in the pipeline structure with sub-millimeter accuracy ➡️ Comprehensive Data: Provides in-depth information about the pipeline’s condition, aiding in effective maintenance and repair decisions ➡️ Safety & Efficiency: Prevent pipeline failures, reduce risks, and improve safety for both operators and the environment. Heading to PPIM 2025? Stop by our booth #305 to check out our 3D tool model and learn more about our advanced technologies. Our experts will be on hand to answer any questions and give you insights into how ART Scan can transform your pipeline inspection strategy. Visit our website to learn more about how we are safeguarding pipelines with ART Scan: https://hubs.ly/Q033L-800 -- #PipelineInspection #AcousticResonanceTechnology #UltrasonicTesting #PipelineSafety #PPIM2025

The S-transform (ST) aka Stockwell-Transform is a time-frequency representation known for its local spectral phase properties. ST combines the advantages of STFT (short time fourier transform) and Wavelet Transform (WT) by providing both time and frequency localization without a fixed window size. This results in a more adaptive time-frequency resolution, where it can adjust according to the local characteristics of the signal. ST allows one to define the meaning of phase in a local spectrum setting, and results in many advantageous characteristics. Centrifugal Pumps (CPs) are used in numerous technological applications, including engine manufacturing, air conditioning, chemical processing, electricity generation and more. The abrupt failure of CPs can cause unwanted interruptions or even catastrophic failures which lead to economic losses, long downtime, and costly repairs. To avoid these failures, continuous monitoring of CPs is required. Intelligent Fault Diagnosis (IFD) must be used to quickly detect soft faults and 34% of them are caused by faults with the mechanical seal (MS). Soft CP faults, such as fluid flushing, shaft wear, fretting, etc, are caused by defective MS. The highly non-stationary vibration signals from CPs used in IFD must be pre-processed to extract relevant fault-related features, since fault-related features are frequently obscure and disguised by the signals’ considerable amounts of noise and fading fault impulses. Time, frequency, and time-frequency-domain (TFD) analyses are the three basic methods for signal processing. The problem with STFT, is that the higher time resolution might result in lower frequency resolution and vice versa. WT can address the STFT’s resolution issues, since it employs larger windows at lower frequencies and smaller windows at higher frequencies. The problem with WT, it is noise-sensitive and lacks phase information for the analyzed signals. To overcome the challenges of using STFT and WT for the pre-processing of the highly non-stationary CP signals, the authors of [1] proposed a framework (see Fig-1 on page 1), where S-transform is used instead. • The raw signal is first filtered to a chosen fault-specific frequency band. • ST (S-Transform) is then applied to this band, yielding scalograms that depict energy fluctuations across different frequencies and time scales, represented by color intensity variations. • A Sobel filter is used on the S-transform scalograms, resulting in the generation of novel Sobel Edge scalograms. These Sobel Edge scalograms aim to enhance the clarity and discriminative features of fault-related information. • These new scalograms are then provided to a convolutional neural network (CNN) for the fault classification of centrifugal pumps. The proposed CP fault classification method outperformed SOTA (state of the art) reference methods that reached an accuracy of 99.68% overall. The link to their paper [1] is posted in the comments.

Check out how Kenera used the FARO Freestyle 2 for their Brownfield Engineering Transformation Solution! They: 🌐 Surveyed specific site areas with compromised items (like pipework defects) 🌐 Transfered scanned data to the in-house team for modeling 🌐 Placed magnetic targets around the site to create common reference points 🌐 Used point clouds to assist engineering design and precision Learn more about how they saved up to 93% savings in operations costs, had a 85% decrease in exposure risk for personnel, and had 80% faster scan capture than traditional tripod scanners here: https://lnkd.in/ecMNM-FD #Freestyle2 #PointClouds #Modification #Data #Engineering #BuildingTogether

Introducing our New Data Logger with 7" Touchscreen! Revolutionize your data acquisition and control processes with our cutting-edge data logger. Equipped with a user-friendly 7-inch touchscreen, it offers seamless interaction and intuitive navigation. #KeyFeatures: Versatile Sensor Integration: - Easily connect a wide range of sensors thanks to our integrated multiple sensor interface unit. - Intelligent Automation: Benefit from our advanced Automatic PID-based motor control operation for precise and efficient processes. - Comprehensive Data Logging: Capture and analyze critical data with high-resolution ADC and load cell interface capabilities. - Modular Design: Customize your data logger to meet specific requirements with our highly configurable and modular hardware and software. #IdealApplications: #MaterialTesting: Compression Testing, Tensile Testing, Universal Testing, Flexure Testing #SoilTesting: Soil Consolidation, Shear Tests, Triaxial Tests #Manufacturing: Quality Control, Process Monitoring, Machine Automation #ResearchandDevelopment: Lab Experiments, Data Analysis Whether you're in manufacturing, research, or any industry that relies on accurate data and automated control, our data logger is the ideal solution. #dataacquisition #automation #industrialsensors #touchscreen #pidcontrol #datalogging #techmarvy #materialtesting #soiltesting #manufacturing #research #ctm #utm #tensile #civiltesting #quality

ProseraPod: Ask Greg McMillan 🎙 What role do you see dynamic simulation playing in the future of developing the best Temperature Measurement Specifications? Greg's Response:🎙 Process efficiency and capacity are often determined by a temperature control loop performance’s effect on the formation and purification of the product. Good composition control in biological and chemical reactors and in distillation columns is achieved by tight temperature control. There is a significant misunderstanding as to the best selection, specification, and installation of temperature measurements aggravated by the lack of inclusion of the 5Rs (resolution, repeatability, rangeability, response time, and reliability), drift, and nonlinearity in measurement specifications. The inclusion of the 5Rs in dynamic simulations can help motivate the use of the best temperature measurements. Dynamic simulations should include several time constants that are the thermal lags associated with the thermowell, modeling objects to simulate the sensor resolution and repeatability, a steadily increasing error to simulate sensor drift, and a constant signal to simulate sensor failure. The detailed following list of Top Ten Mistakes alerts us to the significance and extent of the problem besides directing us to the best temperature measurement. 💡 Top Ten Mistakes in Temperature Measurement Specification 💡 ❌ 1. Thermocouple (TC) instead of Resistance Temperature Detector (RTD) for < 1600 oF. ❌ 2. Direct wiring of sensor to TC or RTD I/O cards. ❌ 3. Loose fit of sensor in thermowell. ❌ 4. Sensor not touching bottom of thermowell. ❌ 5. Focus on TC versus RTD sensor instead of thermowell response time. ❌ 6. Thermowell length and installation results in sensor seeing jacket temperature. ❌ 7. Thermowell length and installation results in low velocity at tip. ❌ 8. Transmitter span too large. ❌ 9. Transmitter remote mounted. ❌ 10. Thermowell wall at tip too large   ✅ The best temperature measurement uses a head mounted narrow span RTD transmitter on a stepped thermowell with a tight fit spring-loaded sheathed platinum resistance temperature detector (PRTD) mounted in a piping elbow to sure sufficient length and velocity (e.g., liquids > 5 fps). The ISA book Advanced Temperature Measurement and Control Second Edition details how to get the best temperature measurement and control. Click below to see a Table 2.8 from the book 💠 Advanced Temperature Measurement and Control Book - https://lnkd.in/gx8mAaVB 💠 ProseraPod - https://lnkd.in/gRmHGyqv #Prosera #ProseraPod #InnovateAtProsera #ProcessControl #ProcedureAutomation #AskGregMcMillan #DigitalTwins