Pipe Insulation ISO 15665 Performance Modeling
Time: 7:40 pm
Author: Kevin Herreman
Abstract ID: 1868
Reducing industrial noise emission utilizing jacketed pipe insulation is critical to reducing noise in industrial spaces. The ISO 15665 standard defines a testing process for measurement of the acoustical performance of installed and jacketed pipe insulation systems. However, the cost of testing per this standard, especially when using an external laboratory, can be very costly. That makes the development of a model to accurately estimate the performance of single, and multilayered, jacketed pipe insulation highly desirable. Utilizing a one-dimensional theoretical acoustic model along with empirical data, a model with sufficient accuracy to provide insertion loss results relative to the ISO 15665 standard was created. The creation and resulting functionality of the model for determining jacketed pipe insulation insertion loss and comparison of the resulting data to test results will be discussed herein.
Modeling ISO 156656 Large Diameter Industrial Pipe Insulation
Time: 8:00 pm
Author: Kevin Herreman
Abstract ID: 1870
As previously presented, reducing industrial noise emission utilizing jacketed pipe insulation is critical to reducing noise in industrial spaces. The ISO 15665 standard defines a testing process for measurement of the acoustical performance of installed and jacketed pipe insulation systems. To provide a cost-effective method for evaluating various types of multilayered jacketed pipe insulation a model was developed. The model accurately estimates the performance of single, and multilayered, jacketed pipe insulation. Validating the use of the model to very large pipe diameters is highly desirable as the cost to test is significantly higher than testing the medium or small diameter pipe insulation. The estimated insertion loss result from the model is compared to validation testing results for large diameter jacketed pipe insulation are reported herein.
Ferry M/V Kramer noise mitigation
Time: 8:40 am
Author: Michael Bahtiarian
Abstract ID: 2060
The Motor Vessel (M/V) Edward V. Kramer is an aluminum vessel that operates as a small passenger ferry, which is owned and operated by the Department of Homeland Security (DHS) and used to transport DHS personnel and materials to Plum Island, NY. It was placed in service in 2018 and right from the start the sound levels inside the Main Deck compartment were found to be excessive. The original vessel specification included a noise limit of 75 dBA in the Main Deck Passenger Lounge and measured levels were as high as 87 dBA. A ship survey of sound and vibration was performed. Noise predictions to determine the controlling sound paths was also performed based on engine sound and vibration source levels. Recommendations for mitigation were presented and carried out by another shipyard. Mitigation included vibration isolation of the main engines and sound attenuation improvements to the Main Deck Passenger Lounge. After completion of the modifications, another survey was performed in 2021 and results show a reduction by as much as 11 dB in the Main Deck Passenger lounge. Noise estimation methods and details on the noise control treatments are given in the paper.
Partial discharge monitoring using deep neural networks with acoustic emission
Time: 6:40 am
Author: Saichand Gourishetti
Abstract ID: 2373
The occurrence of partial discharge (PD) indicates failures in electrical equipment. Depending on the equipment and operating conditions, each type of PD has its own acoustic characteristics and a wide frequency spectrum. To detect PD, electrical equipment is often monitored using various sensors, such as microphones, ultrasonic, and transient-earth voltage, whose signals are then analyzed manually by experts using signal processing techniques. This process requires significant expertise and time, both of which are costly. Advancements in machine learning, aim to address this issue by automatically learning a representation of the signal, minimizing the need for expert analysis. To this end, we propose a deep learning-based solution for the automatic detection of PD using airborne sound emission in the audible to the ultrasonic range. As input to our proposed model, we evaluate common time-frequency representations of the acoustic signal, such as short-time Fourier, continuous wavelet transform and Mel spectrograms. The extracted spectrum from the PD signal pulses is used to train and evaluate the proposed deep neural network models for the detection of different types of PD. Compared to the manual process, the automatic solution is seen as beneficial for maintenance processes and measurement technology.
Power generator noise evaluation considering conversation audibility and improvement
Time: 8:40 pm
Author: Junji Yoshida
Abstract ID: 1927
Compact power generators are useful and convenient tools all over the world. The products are mainly set at around living space of human. Hence, the radiated noise should not disturb the human activities such as conversation. In this study, we then focused on the ease of conversation as the power generator noise performance and attempted to improve the performance. We firstly carried out subjective evaluate tests using recorded generator noise samples and reproduced Japanese syllables to evaluate the performance quantitatively from sound pressure of power generator noise. In the test, the participants answered the syllable they heard under the reproduced generator noise condition. And the correct answer rate of the presented syllable was calculated in each generator noise. The correct answer rate could be expressed well by using articulation index (AI) of each generator noise. Subsequently, the noise reduction target level of a portable generator satisfying the rate at 80% was set in each frequency band considering the influence of each frequency band on AI. Noise countermeasure was carried out to intake and exhaust parts having large contribution at the reduction target frequency bands. Finally, the noise could be decreased well and the AI cleared the target level.
Machining and fabrication equipment in workplaces
Time: 8:20 pm
Author: William Rosentel
Abstract ID: 3102
Increasingly well-developed workplace acoustic standards have resulted in more consistent outcomes across projects and normalized occupant expectations of acoustic quality, enhancing productivity and satisfaction. Yet these standards are often not developed for or applied to R&D and manufacturing spaces that include traditional workplace room types and uses; design criteria is limited to OSHA-assessment for noise-at-work violations. Hybrid office buildings incorporating prototyping and maker spaces are common today and often contain high-noise equipment traditionally found in dedicated machine shops. As these facilities are incorporated alongside traditional offices, noise and vibration levels generated by fabrication equipment should be accurately quantified to avoid compromised workplace acoustics. While sound data is available for most large construction equipment, available data for smaller fabrication machines typically found in machine shops is often non-standardized and difficult to obtain. Field measurement of existing equipment installations can ground an acoustical analysis with real-world data and be highly valuable in evaluating potential noise and vibration impacts and applying cost-effective mitigation during design. This case study will present measurements obtained during a noise and vibration assessment of an existing machine shop located within an office building. The discussion will include limitations of the data and an assessment of potential for disruptions.