## 3 State-of-the-Art Techniques for Predictive Maintenance
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Name** & **Description** \\ \hline
[132, 133] & NASA Turbofan Dataset-CMAPSSD and & The turbofan engine degradation simulation dataset, \\  & CMAPSSD-2 & generated with the Commercial Modular Aero-Propulsion \\  & & System Simulation (C-MAPSS) dynamical model. \\ \hline
[134] & PHM 2008 Dataset & The degradation collected from aircraft engines derived from CMAPSSD. \\ \hline
[135] & NASA Ames Milling Dataset & Acoustic emission, vibration, and motor current data collected under different experimental conditions for predicting the milling tool wear. \\ \hline
[136] & NASA Bearing Dataset & Run-to-failure vibration data from 4 accelerometers in a shaft. \\ \hline
[137] & Case Western Reserve University (CWRU) Bearing Dataset & Test rig operating with different load conditions. \\ \hline
[138] & FEMTO Ball Bearing Dataset from IEEE & Run-to-failure temperature and vibration data from engine \\  & PHM Challenge & thermocouple and accelerometer sensors. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Benchmark datasets for PdM tasks. Table 2 shows a taxonomy for PdM applications in different problem types/approaches from various industries. The second column of Table 2 mainly indicates a class name relating to the repair and maintenance activities from [149]. Table 3 summarizes the study numbers in Table 2. As seen in Table 3, the electrical equipment class of the repair and maintenance field is the most popular in AI and maintenance, with 30 studies focused on this field. The machinery category is an essential topic with ten studies and attracts wide attention in this field. The classes of electronic and optical equipment and fabricated metal products attract attention with nine and eight studies, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Name** & **Description** \\ \hline
[139] & Roll Bearing Dataset from IEEE PHM Challenge & A training set of six rolling bearings operated in three different conditions, and a testing set of 11 more. \\ \hline
[140] & Backblaze Hard Disk Drive Dataset & The daily status of hard disk drives (HDDs), consisting of 433 failed drives and 22,962 good drives. \\ \hline
[141] & PAKDD2020 Alibaba AI OPS Competition Dataset & HDD daily health status data including both a raw and a normalized value as well as a label and the time of failure. \\ \hline
[142] & NASA Ames Prognostics Dataset & Li-ion battery degradation data during repeated charge and discharge cycles. \\ \hline
[143] & Lithium-ion Battery Dataset of the University of Maryland & The current and voltage data on different EV drive cycles at varying ambient temperatures (including 0 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 45 \({}^{\circ}\)C). \\ \hline
[144] & MOSFET Thermal Overstress Aging & Run-to-failure experiments on power MOSFETs under thermal overstress. \\ \hline
[145] & MAFAULDA & Fault measurements from machinery simulators run under different load conditions. \\ \hline
[146] & Microsoft Azure PdM Dataset & Data modules of machines, telemetry, errors, maintenance, and failures collected by a Microsoft employee for PdM modeling collection. \\ \hline
[147] & Global Energy Forecasting Competition (GEFCOM) Dataset & Hourly solar power generation data and assigning numerical weather forecasts from 1 April 2012 to 1 July 2014. \\ \hline
[148] & The UCI SECOM Dataset & Measurements of features of semiconductor production within a semiconductor manufacturing process. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._