Machine diagnosis using acoustic analysis: a review

Shaikh, Kader B T; Jawarkar, Naresh P.; Ahmed, Vasif

doi:10.1109/21cw48944.2021.9532537

Cited by 9 publications

(6 citation statements)

References 34 publications

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“…The development of other acoustic methods has also been widely used in fault detection for various industrial systems, such as motors [ 72 ], pumps [ 73 ], drills [ 74 ], and gearboxes [ 75 ]. SMOFS and MSAF are both frequency methods in which fault features are automatically selected based on the differences in frequency between the spectrum components [ 76 ].…”

Section: Review Of Fd Methods Based On Shallow Machine Learning and D...mentioning

confidence: 99%

A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models

Alharbi

Luo

Zhang

et al. 2023

Sensors

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Due to increasing demands for ensuring the safety and reliability of a system, fault detection (FD) has received considerable attention in modern industries to monitor their machines. Bulk materials are transported worldwide using belt conveyors as an essential transport system. The majority of conveyor components are monitored continuously to ensure their reliability, but idlers remain a challenge to monitor due to the large number of idlers (rollers) distributed throughout the working environment. These idlers are prone to external noises or disturbances that cause a failure in the underlying system operations. The research community has begun using machine learning (ML) to detect idler’s defects to assist industries in responding to failures on time. Vibration and acoustic measurements are commonly employed to monitor the condition of idlers. However, there has been no comprehensive review of FD for belt conveyor idlers. This paper presents a recent review of acoustic and vibration signal-based fault detection for belt conveyor idlers using ML models. It also discusses major steps in the approaches, such as data collection, signal processing, feature extraction and selection, and ML model construction. Additionally, the paper provides an overview of the main components of belt conveyor systems, sources of defects in idlers, and a brief introduction to ML models. Finally, it highlights critical open challenges and provides future research directions.

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Section: Review Of Fd Methods Based On Shallow Machine Learning and D...mentioning

confidence: 99%

A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models

Alharbi

Luo

Zhang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…To detect equipment failures, many traditional time series signal anomaly detection methods are introduced in [18]. A machine anomaly detection method based on analyzing acoustic signals is summarized in [19]. Demegul et al [20] also explored the possibility of using the short-time Fourier transform (STFFT) to diagnose the misalignment problem of the linear feed axis.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the misalignment of machine tools with autoencoders after they are trained with transfer learning data

Demetgul,

Zheng,

Tansel

et al. 2023

Int J Adv Manuf Technol

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CNC machines have revolutionized manufacturing by enabling high-quality and high-productivity production. Monitoring the condition of these machines during production would reduce maintenance cost and avoid manufacturing defective parts. Misalignment of the linear tables in CNCs can directly affect the quality of the manufactured parts, and the components of the linear tables wear out over time due to the heavy and fluctuating loads. To address these challenges, an intelligent monitoring system was developed to identify normal operation and misalignments. Since damaging a CNC machine for data collection is too expensive, transfer learning was used in two steps. First, a specially designed experimental feed axis test platform (FATP) was used to sample the current signal at normal and five levels of left-side misalignment conditions ranging from 0.05 to 0.25 mm. Four different algorithm combinations were trained to detect misalignments. These combinations included a 1D convolution neural network (CNN) and autoencoder (AE) combination, a temporal convolutional network (TCN) and AE combination, a long short-term memory neural network (LSTM) and AE combination, and a CNN, LSTM, and AE combination. At the second step, Wasserstein deep convolutional generative adversarial network (W-DCGAN) was used to generate data by integrating the observed characteristics of the FATP at different misalignment levels and collected limited data from the actual CNC machines. To evaluate the similarity and limited diversity of generated and real signals, t-distributed stochastic neighbor embedding (T-SNE) method was used. The hyperparameters of the model were optimized by random and grid search. The CNN, LSTM, and AE combination demonstrated the best performance, which provides a practical way to detect misalignments without stopping production or cluttering the work area with sensors. The proposed intelligent monitoring system can detect misalignments of the linear tables of CNCs, thus enhancing the quality of manufactured parts and reducing production costs.

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“…Early fault diagnosis can optimize maintenance schedules while maximizing machine utilization and avoiding catastrophic damage [3]. Acoustic signals are an important source of information reflecting the operating status of equipment, and acoustic signals have the advantages of easy acquisition, noncontact measurement, and low cost [4]. Sound signal analysis has become an effective method for monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…At first, the condition monitoring of the machine relied on the experience of skilled technicians, but the decision-making process was highly subjective [5]. This depends on the operator's experience and ability to correctly hear and perceive sounds [4], so more automated methods are needed, and automatic audio classification is of great significance in today's era.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis System for a Pipeline Robot Based on Sound Signal Recognition

Cao

Wang

et al. 2022

Sensors

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Timely and accurate identification of fault types at the early stage of minor faults is significant for cutting off fault evolution. In order to have a clear understanding of the pipeline robot’s own situation in the pipeline, this paper proposes a fault diagnosis system for pipeline robots based on sound signal recognition. This can effectively reduce the probability of serious faults such as shutdown and loss of control in the pipeline without affecting the safe operation of the pipeline robot, which is a key issue to improve the reliability of the pipeline robot. The system consists of a combination of three parts: hardware, software, and algorithm. On the one hand, Raspberry Pi is the core module, while on the other hand, it is also responsible for the data transmission between the various modules, including storing the original sound signals collected by the sensors and transmitting the diagnosis results to the upper computer software interface. The proposed system is validated on the dataset collected by the data experimentation platform. The experimental results show that the proposed fault prediction method obtains advanced results on this dataset, verifying the effectiveness and stability of the proposed fault diagnosis system for pipeline robots based on sound signal recognition.

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Machine diagnosis using acoustic analysis: a review

Cited by 9 publications

References 34 publications

A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models

A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models

Monitoring the misalignment of machine tools with autoencoders after they are trained with transfer learning data

A Fault Diagnosis System for a Pipeline Robot Based on Sound Signal Recognition

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