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Investigating Audio Signatures of Pump Failure for Predictive Maintenance in Utility Environments

Abrahamsson, Joel LU and Möller, Markus LU (2026) In Master's Theses in Mathematical Sciences FMAM05 20261
Mathematics (Faculty of Engineering)
Abstract
Damaged or partially damaged pumps can cause issues that at best is an inconvenience and at worst a catastrophe. In order to prevent such issues it is necessary to catch any potential damage as early as possible. One possible solution is to use a sensor that can be placed on or near a pump in order to monitor it using audio. Audio based fault detection is still a relatively new field of research, with a wide verity of possible methods. In this study we assess some of them and compare them in a verity of tests in order to try and evaluate their strengths and weaknesses. This is done using two datasets, the MIMII datasets and a dataset we constructed ourselves that contain a verity of faults. Our results show that it is possible to detect... (More)
Damaged or partially damaged pumps can cause issues that at best is an inconvenience and at worst a catastrophe. In order to prevent such issues it is necessary to catch any potential damage as early as possible. One possible solution is to use a sensor that can be placed on or near a pump in order to monitor it using audio. Audio based fault detection is still a relatively new field of research, with a wide verity of possible methods. In this study we assess some of them and compare them in a verity of tests in order to try and evaluate their strengths and weaknesses. This is done using two datasets, the MIMII datasets and a dataset we constructed ourselves that contain a verity of faults. Our results show that it is possible to detect faults to a very high accuracy in a controlled environment but difficulty arises when these models are used outside of the controlled environment. Our conclusion is that fault detection using audio and machine learning bears much potential but there are some key challenges that must be either overcome or accommodated. (Less)
Please use this url to cite or link to this publication:
author
Abrahamsson, Joel LU and Möller, Markus LU
supervisor
organization
course
FMAM05 20261
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Audio based fault detection, Fault detection, Audio classification, Predictive maintenance, MIMII
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3620-2026
ISSN
1404-6342
other publication id
2026:E35
language
English
id
9237295
date added to LUP
2026-06-16 10:15:09
date last changed
2026-06-16 10:15:09
@misc{9237295,
  abstract     = {{Damaged or partially damaged pumps can cause issues that at best is an inconvenience and at worst a catastrophe. In order to prevent such issues it is necessary to catch any potential damage as early as possible. One possible solution is to use a sensor that can be placed on or near a pump in order to monitor it using audio. Audio based fault detection is still a relatively new field of research, with a wide verity of possible methods. In this study we assess some of them and compare them in a verity of tests in order to try and evaluate their strengths and weaknesses. This is done using two datasets, the MIMII datasets and a dataset we constructed ourselves that contain a verity of faults. Our results show that it is possible to detect faults to a very high accuracy in a controlled environment but difficulty arises when these models are used outside of the controlled environment. Our conclusion is that fault detection using audio and machine learning bears much potential but there are some key challenges that must be either overcome or accommodated.}},
  author       = {{Abrahamsson, Joel and Möller, Markus}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Investigating Audio Signatures of Pump Failure for Predictive Maintenance in Utility Environments}},
  year         = {{2026}},
}