International Journal of COMADEM

What drives accuracy for video vibration?

William Marscher, Andrew Lerche, Sergey Frolov — Mon, 29 Apr 2024 00:00:00 +0000

Vibration detection and display using magnification, or amplification, of high- speed video (Vibration Video Amplification, VVA) has been demonstrated to be a very useful tool. VVA has been challenged to produce useful results, especially when lower quality cameras are used, or if the component being tested involves relatively high frequencies (e.g. 200 Hz or higher). For these cases, detection of very low displacement levels is required for VVA to give useful results. This is because video methods detect displacement, not velocity, and vibration standards, such as ISO 20816 or HI 9.6.4, are based on velocity. For a given velocity acceptance level, the displacement associated with that velocity decreases in proportion to the vibration frequency. At relatively low frequencies such as 50 or 60 Hz, the resulting displacements are easily detectable. However, at higher frequencies such as gas turbine generator (“high spool”) operating speeds, or pump or compressor blade passing frequencies, it is much more challenging. At 500 Hz for example, the detection capability required for vibration detection is roughly equal in terms of mils versus in/sec rms. Therefore, to detect 0.2 in/sec rms (5.1 mm/s), a displacement detection accuracy of 0.2 mil p-p (about 5 microns p-p) is required. This is achievable, but challenging for most cameras and scene evaluation software. Therefore, this paper will discuss methods to improve and evaluate the displacement resolution of VVA. The encouraging degree of precision that has been able to be achieved is reported.

Asset health management utilizing batch multivariate pattern analysis

Chance M Kleineke, Michael T Santucci — Mon, 29 Apr 2024 00:00:00 +0000

Budgetary and performance demands have led many power utility businesses to focus on some form of centralized fleet monitoring. Advanced pattern recognition (APR) is a common tool used for monitoring the major assets involved in the production of electricity in this manner. While pattern recognition techniques can focus on real-time steady-state operation and ease the challenge of monitoring hundreds or thousands of pieces of equipment, many assets can incur damage during the start-up and shutdown transient conditions that are not as commonly watched. This damage leads to failure over time, but signs of the damage may not be apparent during steady-state operation. A multivariate pattern analysis was designed to identify anomalies specific to start-up and shutdown data of these assets, along with any other batch production process with defined start and end parameters. This method can be used across all types of processes and equipment. An early warning case study was conducted with a major power utility to validate the technique on a forced outage caused by a steam turbine bearing failure. Data was provided for two units with multiple coast downs over several months. This case study examines normal operation for monitored variables, such as temperatures and vibration readings, with the goal of detecting an individual variable’s deviation from acceptable conditions and identifying the overall impact each has on the process. The study shows how this technique may be used to increase the early detection of faults and can be used in conjunction with other APR and diagnostic tools to help a business improve overall asset health management.

Change detection for improved maintenance notification

Eric Bechhoefer, Joelle Kessler — Mon, 29 Apr 2024 00:00:00 +0000

HUMS (Health and Usage Monitoring Systems) provide enhanced safety and availability by alerting maintenance personnel when components (shaft/gears/bearings) have degraded or become damaged. Proactive maintenance ensures that a degraded component will not fail in flight. Often, the damage is from high cycle fatigue or other relatively slow degradation processes. However, some events, such as maintenance, result in a step-change in the component's health. While high cycle fatigue degradation can be trended, a step-change in component health presents a challenge. This paper describes the development of two methods for step-change detection. These methods would allow the automated capture of maintenance or other events resulting in a step-change in component health.

Separation of vibratory components in complex systems for condition monitoring

Fadi Karkafi, Dany Abboud, Quentin Leclère, Jérôme Antoni, Mohammed El Badaoui — Tue, 07 May 2024 00:00:00 +0000

Mechanical signals are often a mixture of multiple components produced by different sources within multiple subsystems. The separation of these components is beneficial for differential diagnosis, fault severity assessment and prognosis. The synchronous average (SA) is a powerful and widely used technique for this purpose, providing optimal estimation performance and simplicity. However, the complexity of such systems often implies the existence of several spectral interferences, which jeopardize the straightforward application of the SA. This paper addresses this issue by proposing a general strategy based on the synchronous average and the set theory, to isolate each vibratory contribution related to a source of interest, while accounting for spectral interferences. This approach enables a comprehensive understanding of the system’s health status and the ability to locate specific issues within a subsystem. The proposed methodology is demonstrated through an example of an accessory gearbox vibration signal acquired during a ground test campaign on a CFM56 turbojet engine.

Condition monitoring of reciprocating compressor valve health via a statistical time-frequency approach

Jacob Chesnes, Jason Kolodziej, Michael Anderson, Daniel Nelson — Mon, 29 Apr 2024 00:00:00 +0000

The goal of this work is to present a time-frequency-based approach to vibration condition monitoring of gas compressor valves. Due to the cyclostationary nature of reciprocating compressors frequency analysis alone is typically insufficient to assess valve condition. Experimental data is collected on an instrumented Dresser-Rand ESH-1 dual-acting reciprocating compressor by seeding known leakage fault severity levels in the suction and discharge manifold valve assemblies. Fault severity is measured by the leakage hole area and the number of damaged valve elements. By applying a short-time Fourier transform signal processing method to externally mounted vibration measurements, it is possible to identify key regions of interest in the compression cycle. Statistical features are then extracted from these regions and used to train a Bayesian classifier. The presented machine learning approach achieves greater than 90% success in assessing the valve leakage health of the compressor.

Identifying machinery anomalies using shape identification and classification algorithms

Mantosh Bhattacharya, Arnav Bhattacharya — Mon, 29 Apr 2024 00:00:00 +0000

The prime responsibility of anomaly detection and mitigation of rotating machinery distress lies with a certified vibration analyst. Vibration analysis is a multi-layered task and with huge number of assets in a large plant, the work can become tedious. With the introduction of a machine learning algorithm, the first layer of the task can be offloaded to an AI based module. The direct unfiltered numerical values of vibration with units such as mm/sec RMS, ips RMS, micron pk-pk, mils pk-pk which is normally used as marker for alarm and trip values. However, for anomaly detection, vibration signatures are analyzed in forms of various plots like Lissajous (Orbit) plots, band based FFT spectrums and shaft centerline plots. This paper presents a road map for detection of anomalies using a simplified multi-layered neural network technique based on geometrical functions of vibration signature plots. From a continuous streaming data, the algorithm can detect the plots of concern from a dataset of synthetic images designed to benchmark systems for understanding the spatial and logical relations among multiple shapes. This paper deals only on the cyclo-stationary-periodic signal for maintaining simplicity in understanding for readers.

Mitigation of excess vibration at motor NDE due to resonance with tuned mass damper

Meng Hee Lim, Zair Asrar, Kee Quen Lee, Mohd Salman Leong — Mon, 29 Apr 2024 00:00:00 +0000

This paper presents a case study for fault diagnosis and mitigation of excessive motor vibrations caused by floor-induced resonance. Vertical motors in a water treatment plant had experienced excessive vibration levels (> 12 mm/s) at its non-drive end (NDE) location. A comprehensive vibration investigation was conducted to map-out the vibrations of the entire machine train which includes pipes, coupling, motors, pedestal, plinth and floor slab of the pump house. Vibration investigation found that the root cause of the excessive motor vibration was caused by the resonance of the floor slab as the natural frequency of the floor slab (20 Hz) coincided with the operating frequency (25 Hz) of the motors. As such, the first remedy action undertaken at that time was the reinforcement of the motor pedestal with additional ribs in order to shift the primary natural frequency of the motor-pedestal system to be away from the motor operating frequency. However, this attempt failed to subside the excessive motor vibration as this method did not result in significant shift in the natural frequency of the motor-pedestal-floor system. The second attempt was conceived to design a tuned-mass damper (TMD) for the motors. Finite Element Analysis was used to design a custom made TMD to be attached at the motor NDE locations. After several fine-tuning and modifications, the TMD was installed and had successfully reduced the motor NDE vibration levels for the vertical motors to be within 5 mm/s for all tri-axes measurements and thus enables the motors for long term unrestricted operation in accordance with the ISO 10816-3 Standard.

Development of a Risk Analysis Method using a Hybrid of WBS and PESTLE on a Construction Project

Dewa Ketut Sudarsana, Anak Agung Diah Parami Dewi, Ariany Frederika — Mon, 29 Apr 2024 00:00:00 +0000

The risk identification stage that is a part of the risk analysis in construction project implementation mostly uses the Work Breakdown Structure (WBS), political, economic, social, technological, legal, and environmental (PESTLE) methods, or other partial methods. Partial risk identification with WBS or PESTLE does not yet provide adequate risk mapping, so it needs to be studied using a hybrid risk identification method between WBS and PESTLE. Qualitative descriptive methods were used in this research. The WBS and PESTLE hybrid risk identification methods were developed through a review of the literature and expert judgment. The risk assessment and evaluation of risk levels for qualitative risk analysis used the Probability-Impact (PI) method. The hybrid WBS and PESTLE risk identification method obtained was in the form of a WBS and PESTLE matrix framework model. The results of implementing risk identification based on the WBS and PESTLE risk sources provided more in-depth information on each task in the WBS, mapped to the PESTLE risk sources. The results of the analysis of the major risk categories consisting of unacceptable and undesirable risk levels can be traced to the WBS and PESTLE tasks. For construction project teams, the hybrid WBS and PESTLE method makes it easier to identify and map risks in detail so then the results of the risk analysis and evaluation can be handled and controlled in detail.

Influence of weight bearing resistance training on explosive power of lower limbs in volleyball

Hao Chen — Mon, 29 Apr 2024 00:00:00 +0000

This study aims to study the effect of weight resistance training (weight resistance vibration training) on the explosive power of lower limbs in volleyball for students in College of General Studies of Chongqing City Vocational College, so as to explore the advantages of weight resistance training and promote the development of weight resistance training. Methods: Literature method and mathematical statistics method were used in this study. Firstly, relevant knowledge of weight-bearing resistance training was sorted out through relevant academic websites to lay a theoretical foundation for the selection of later research indicators. Then, taking students of College of General Studies of Chongqing City Vocational College as the research objects, SPSS24.0 software was used to conduct statistical experiments, and the difference between the effects of single-leg weight-bearing anti-resistance vibration training, two-leg weight-bearing anti-resistance vibration training and traditional weight-bearing anti-resistance vibration training (non-vibration two-leg weightbearing anti-resistance vibration training) was compared and analyzed. Results: The experimental results show that the effect of single leg weight resistance vibration training and double leg weight resistance vibration training is better than the traditional weight training group, which can greatly improve the level of explosive power of lower limbs in volleyball. Conclusion: It shows the importance of single leg weight anti-resistance vibration training and double leg weight anti-vibration training in volleyball, and should be promoted vigorously.

ADAPTIVE data analytics – the issue of data quality in self-service analytics

Laurence RL Gartner, HF Swanepoel, JHC Pretorius — Mon, 29 Apr 2024 00:00:00 +0000

The growing ICT skill shortages that have been plaguing the economy for years are likely to get worse. To combat this, there is a growing trend in the industry towards self-service data preparation, where the key objective is to build intelligent systems that enable business analysts and data scientists to prepare ad-hoc data sets themselves without needing help from IT staff. Systems and applications built by non-ICT professionals can raise issues. Once these end-user systems find their way into the daily operational ‘life’ of corporations, they can become integrated parts of key processes. Before long, it is adopted as part of vital production process outcomes. The ICT department may find itself eventually burdened by having to support something they never built, tested, or approved of as being part of their suite of production systems let alone corporate strategy. Whether this plays out or not, a more immediate concern is raised regarding the data integrity and process integrity of such ‘self-serving’ systems. How are these addressed by those operating outside the sphere of ICT? When it comes to diagnostic condition monitoring and data analytics, as well as decision-making in the engineering management environment, the size of data involved in decision making are significant, making it even more vital that data analytics and business insights can be gained fast using ICT capability that supports technical professionals in these business disciplines. Since 2013-2020, engagement with Universities, ASX listed companies, the Australian Federal Government, State Government, Australian Government Agencies, private companies, businesses, and individuals, many times as an auditor, has also assisted the researcher with gaining the insights and disciplines required for dealing with these types of challenges. The paper addresses the issue of quality control in the form of data integrity and process integrity for end-user (self-serving) built systems. It demonstrates how these challenges can be tackled by referring to one such self-service model that has addressed this quality control issue: The Adaptive Analytics Model.