Gearbox fault detection using vibrations

Systems interact with their environment by emitting various signals. These signals are sources of relevant information reflecting equipment functioning. Being able to understand these signals paves the way to significant optimizations.  

For example, before an anomaly or a failure occurs, your machine generates slightly abnormal vibration patterns. By placing a sensor on the machine, we can monitor its activity, and thanks to Machine Learning, we can identify the system’s normal functioning pattern. By analyzing the evolution of vibrations, we can detect any changes, which may be caused by aging or an anomaly. Gearboxes can be very complex and expensive systems. In order to avoid any malfunctioning and to optimize the frequency of the system’s maintenance, we have implemented ML-based anomaly detection, which is the first step towards a predictive maintenance application. This approach can easily be adapted to many industrial machines. 


  • This dataset is from (click the link to get all the information about the dataset)
  • The dataset was made using four vibration sensors placed in different directions on a gearbox test bench. 
  • In this example, we are using 2 files of the dataset. One for a healthy gearbox, one for a broken one.
  • We are using a constant load of 50% but the same results are also reached with various loads. 


4 generic accelerometers (1 axis)


>> Link to the original page of the dataset

>> Download the dataset used in the NanoEdge AI Studio

Regular and Abnormal signals:
– Regular signals: Normal functioning
– Abnormal signals: Faulty gear
Signal length: 512 (4 axes)
Data rate: 416 Hz; Range: 2g


Anomaly detection:
99.67% accuracy, 4.0 KB RAM, 7.1 KB Flash

Blue points correspond to normal signals, red points to abnormal ones. 
The signal numbers are on the abscissa and the confidence of the prediction is on the ordinate