Efficient Fault Diagnosis of Elevator Cabin Door Drives Using Machine Learning with Data Reduction for Reliable Transmission
This article addresses the issue of the elevator cabin door drive system failure diagnosis. The analyzed component is one of the most critical and the most vulnerable part of the entire elevator. Existing solutions in the literature include methods such as spectral analysis of system vibrations, mot...
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/13/7017 |
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| author | Jakub Gęca Dariusz Czerwiński Bartosz Drzymała Krzysztof Kolano |
| author_facet | Jakub Gęca Dariusz Czerwiński Bartosz Drzymała Krzysztof Kolano |
| author_sort | Jakub Gęca |
| collection | DOAJ |
| description | This article addresses the issue of the elevator cabin door drive system failure diagnosis. The analyzed component is one of the most critical and the most vulnerable part of the entire elevator. Existing solutions in the literature include methods such as spectral analysis of system vibrations, motor current signature analysis, fishbone diagrams, fault trees, multi-agent systems, image recognition, and machine learning techniques. However, there is a noticeable gap in comprehensive studies that specifically address classification of the multiple types of system components failures, class imbalance in the dataset, and the need to reduce data transmitted over the elevator’s internal bus. The developed diagnostic system measures the drive system’s parameters, processes them to reduce data, and classifies 11 device failures. This was achieved by constructing a test bench with a prototype cabin door drive system, identifying the most frequent system faults, developing a data preprocessing method that aggregates every driving cycle to one sample, reducing the transmitted data by 300 times, and using machine learning for modeling. A comparative analysis of the fault detection performance of seven different machine learning algorithms was conducted. An optimal cross-validation method and hyperparameter optimization techniques were employed to fine-tune each model, achieving a recall of over 97% and an F1 score approximately 97%. Finally, the developed data preparation method was implemented in the cabin door drive controller. |
| format | Article |
| id | doaj-art-7ca7dbd0bb674de4a9eada9a3f65343a |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7ca7dbd0bb674de4a9eada9a3f65343a2025-08-20T03:28:24ZengMDPI AGApplied Sciences2076-34172025-06-011513701710.3390/app15137017Efficient Fault Diagnosis of Elevator Cabin Door Drives Using Machine Learning with Data Reduction for Reliable TransmissionJakub Gęca0Dariusz Czerwiński1Bartosz Drzymała2Krzysztof Kolano3Department of Electrical Drives and Machines, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 38A, 20-618 Lublin, PolandDepartment of Applied Informatics, Faculty of Mathematics and Information Technology, Lublin University of Technology, Nadbystrzycka 38A, 20-618 Lublin, PolandDepartment of Electrical Drives and Machines, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 38A, 20-618 Lublin, PolandDepartment of Electrical Drives and Machines, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 38A, 20-618 Lublin, PolandThis article addresses the issue of the elevator cabin door drive system failure diagnosis. The analyzed component is one of the most critical and the most vulnerable part of the entire elevator. Existing solutions in the literature include methods such as spectral analysis of system vibrations, motor current signature analysis, fishbone diagrams, fault trees, multi-agent systems, image recognition, and machine learning techniques. However, there is a noticeable gap in comprehensive studies that specifically address classification of the multiple types of system components failures, class imbalance in the dataset, and the need to reduce data transmitted over the elevator’s internal bus. The developed diagnostic system measures the drive system’s parameters, processes them to reduce data, and classifies 11 device failures. This was achieved by constructing a test bench with a prototype cabin door drive system, identifying the most frequent system faults, developing a data preprocessing method that aggregates every driving cycle to one sample, reducing the transmitted data by 300 times, and using machine learning for modeling. A comparative analysis of the fault detection performance of seven different machine learning algorithms was conducted. An optimal cross-validation method and hyperparameter optimization techniques were employed to fine-tune each model, achieving a recall of over 97% and an F1 score approximately 97%. Finally, the developed data preparation method was implemented in the cabin door drive controller.https://www.mdpi.com/2076-3417/15/13/7017classification algorithmsdata transmissionelevatorsfault detectionfault diagnosismachine learning |
| spellingShingle | Jakub Gęca Dariusz Czerwiński Bartosz Drzymała Krzysztof Kolano Efficient Fault Diagnosis of Elevator Cabin Door Drives Using Machine Learning with Data Reduction for Reliable Transmission Applied Sciences classification algorithms data transmission elevators fault detection fault diagnosis machine learning |
| title | Efficient Fault Diagnosis of Elevator Cabin Door Drives Using Machine Learning with Data Reduction for Reliable Transmission |
| title_full | Efficient Fault Diagnosis of Elevator Cabin Door Drives Using Machine Learning with Data Reduction for Reliable Transmission |
| title_fullStr | Efficient Fault Diagnosis of Elevator Cabin Door Drives Using Machine Learning with Data Reduction for Reliable Transmission |
| title_full_unstemmed | Efficient Fault Diagnosis of Elevator Cabin Door Drives Using Machine Learning with Data Reduction for Reliable Transmission |
| title_short | Efficient Fault Diagnosis of Elevator Cabin Door Drives Using Machine Learning with Data Reduction for Reliable Transmission |
| title_sort | efficient fault diagnosis of elevator cabin door drives using machine learning with data reduction for reliable transmission |
| topic | classification algorithms data transmission elevators fault detection fault diagnosis machine learning |
| url | https://www.mdpi.com/2076-3417/15/13/7017 |
| work_keys_str_mv | AT jakubgeca efficientfaultdiagnosisofelevatorcabindoordrivesusingmachinelearningwithdatareductionforreliabletransmission AT dariuszczerwinski efficientfaultdiagnosisofelevatorcabindoordrivesusingmachinelearningwithdatareductionforreliabletransmission AT bartoszdrzymała efficientfaultdiagnosisofelevatorcabindoordrivesusingmachinelearningwithdatareductionforreliabletransmission AT krzysztofkolano efficientfaultdiagnosisofelevatorcabindoordrivesusingmachinelearningwithdatareductionforreliabletransmission |