Servomotor-Driven Ballscrew Mechanism Degradation Data Set

Servomotor-Driven Ballscrew Mechanism Degradation Data Set

Description: This dataset is a record of the inputs, outputs and annotations of a servomotor simulator designed using MathWorks Simulink. The simulation models servomotor operation applied to cases where the motor operates intermittently. One example of such an application is the fine motion control rod drive (FMCRD) mechanism used for intermittent, and typically infrequent, fine motion (insertion or withdrawal) adjustment of control rods in some nuclear reactor designs for optimizing and shaping core power in the reactor. This is in contrast to most rotating machinery that operates on a more continuous basis and sustains wear over steady state operation. Although intermittently operational drives do not run continuously, the servomotors and associated linear motion mechanisms show wear and damage, due to internal and external causes, during their operational lifetime. This causes impediments to the movement of the rotor shaft. The resultant modes of degradation are different from continually operating servomotors. The prognostics and health management for such an intermittent operational regime is different, and this dataset addresses the need for data and algorithms for such applications. Specifically, for machine learning approaches, feature extraction over periods of transient operation requires different preprocessing and signal analysis than features one may extract over continuously operating machinery.

While the causes of servomotor drive degradation can be diverse, in simulation the impact of cumulative damage is modeled as an external opposing load which resists the movement of the motor shaft. This load is a latent unobservable variable, but its effects may be manifested in observable signals from the Simulink model. The loads are a sampled from a mixture of Gaussians with 4 distinct modes leading to an assessment of degradation represented in the dataset as

  1. A categorical degradation level called ‘ylabel’ in the dataset which is one of like-new, low, medium or high, abbreviated as {‘LN’, ‘LO’, ‘MED’, ‘HI’}, and
  2. A floating-point degradation value, denoted ‘DV’ in the dataset.

The dataset is in the form of CSV files, with distinct files for training and testing. Each CSV file contains the following columns which include measurements such as motor currents and rotor speed which would be part of the instrumentation in a typical deployments of rotating machinery. These observable measurements can be used to predict the health state of the servomotor.

Column name Description
time Timestamp for each run
DV Degradation value
rod_demand_pos Demanded rod position
rod_actual_pos Actual rod position
torque Motor torque
rotor_speed Rotor speed
i_3p_a Stator current: Phase A
i_3p_b Stator current: Phase B
i_3p_c Stator current: Phase C
direct Direct component of DQZ transform on the stator currents
quadrature Quadrature component of DQZ transform
run_index Index of runs
transitions Index of transitions
del_pos Demanded displacement, i.e., position delta
ylabel Categorical degradation levels; ylabel ∈ {“LN”, “LO”, “MED”, “HI”}