[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125442-en":3,"doc-seo-125442-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125442,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Machine Learning Framework for Industrial Machine Sound Classification in Predictive Maintenance","Predictive maintenance using anomalous sound classification offers a practical route to identify mechanical faults in industrial machinery. The study presents a machine learning framework for classifying anomalous sounds, emphasizing CT scan machines and fan units. It integrates real CT scan machine audio collected via an IoT microphone and the MIMII dataset, supported by synthetic anomalous data generated through noise injection. Sound signal processing analyzes MFCC and Mel-spectrogram features, training a CNN that reaches about 98% accuracy and supports anticipation and maintenance of normal and abnormal operating conditions.","IEEE RELIABILITY SOCIETY SECTION  \nReceived 5 August 2025, accepted 19 August 2025, date of publication 22 August 2025, date of current version 9 September 2025. Digital Object Identifier 10.1109/ACCESS.2025.3601999  \nMachine Learning Framework for Industrial Machine Sound Classification in Predictive Maintenance  \nNUR FATINAH MD HAFIZ1, SYAMSIAH MASHOHOR1,2,(Member, IEEE), MOHAMMAD HABIB SHAH ERSHAD MOHD AZRUL SHAZRIL 1, AZIZI MOHD ALI1,2, AND MOHD FADLEE A. RASID1,2  \n1Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia 2Wireless and Photonics Networks Research Centre (WiPNET), Faculty of Engineering, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia Corresponding author: Syamsiah Mashohor ([syamsiah@upm.edu.my](syamsiah@upm.edu.my))  \nThis work was supported by Universiti Putra Malaysia through an Incentive Grant 9795500 .  \nABSTRACT Predictive maintenance, utilising anomalous sound classification, demonstrates a strong potential to identify mechanical faults in industrial machinery. This research proposes a machine learning-based framework for classifying anomalous sounds in industrial machines, with a particular focus on CT scan machines and fan units. The study utilises both real-world data from CT scan machine sound and the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset. It offers a comprehensive analysis of sound signal processing techniques, synthetic data generation methods, feature extraction processes, and classification using machine learning models to support predictive maintenance applications. In this research, sound data from a CT scan machine was collected using an Internet of Things (IoT) connected microphone located on the machine in a Klang Valley hospital. Duetothe limited availability of faulty condition data, synthetic anomalous data for both operational and non-operational conditions were generated using a noise injection method. Features derived from Mel Frequency Cepstral Coefficients (MFCCs) and Mel Spectrogram representations were employed to analyse the sound data. The dataset for CT scan machine sounds is categorised into four distinct classes: anomalous operational sound (Aop), anomalous non-operational sound (Anop), normal operational sound (Nop), and normal non-operational sound (Nnop) . In contrast, theMIMII dataset is classified into two categories: normal and abnormal. A Convolutional Neural Network (CNN) model was used for a sound classification system, achieving training accuracies of 98.22% with Mel spectrogram features and 98.12% with MFCC features. The results emphasise the possibility of using CNN-based sound classification to effectively anticipate and maintain CT scan machines. This finding also has the potential to be applied to predictive maintenance applications by detecting both normal and anomalous operating sounds in industrial machinery.  \nINDEX TERMS Artificial intelligence, predictive maintenance, sound signal processing, industrial machine, machine learning, Mel frequency cepstral coefficient, Mel spectrogram, convolutional neural network.  \nI. INTRODUCTION  \nIn the modern era, the healthcare industry is heavily dependent on various types of medical equipment to assist in disease diagnosis, patient monitoring, and rehabilitation. As one of the fastest-growing global sectors, healthcare demands advanced and reliable medical technology [1], [2] . However, exposure to machine failures can pose safety risks  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Haidong Shao .  \nand quality issues in the machinery industry [3] . The research by [4] highlights the importance of maintaining medical equipment in addressing issues such as significant damage and prolonged downtime caused by long-standing problems. Effective maintenance management is crucial to reducing industrial device failures and ","cbCaibSwPDlMXZ8g","https://ap.wps.com/l/cbCaibSwPDlMXZ8g","pdf",2846546,1,16,"English","en",105,"# Introduction\n## Predictive maintenance and sound classification\n## CT scan machine reliability and failure frequency\n# Sound-data framework overview\n## Data sources: CT scan audio and MIMII\n## Synthetic anomalous data generation\n# Feature extraction and classification\n## MFCC and Mel spectrogram representations\n## CNN-based sound classification results","[{\"question\":\"What problem does the proposed framework address in predictive maintenance?\",\"answer\":\"It addresses the need to detect and classify anomalous mechanical conditions in industrial machines by recognizing anomalous sounds for predictive maintenance decisions.\"},{\"question\":\"Which datasets and data collection sources are used for the study?\",\"answer\":\"The study uses real-world CT scan machine sound data collected with an IoT-connected microphone in a Klang Valley hospital and the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset.\"},{\"question\":\"How are anomalous sound samples generated when faulty-condition data is limited?\",\"answer\":\"Synthetic anomalous data is created using a noise injection method for both operational and non-operational conditions.\"}]","Machine Learning Framework for Industrial Machine Sound Classification in Predictive Maintenance | 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problem does the proposed framework address in predictive maintenance?","Question",{"text":75,"@type":76},"It addresses the need to detect and classify anomalous mechanical conditions in industrial machines by recognizing anomalous sounds for predictive maintenance decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which datasets and data collection sources are used for the study?",{"text":80,"@type":76},"The study uses real-world CT scan machine sound data collected with an IoT-connected microphone in a Klang Valley hospital and the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"How are anomalous sound samples generated when faulty-condition data is limited?",{"text":84,"@type":76},"Synthetic anomalous data is created using a noise injection method for both operational and non-operational 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