## 3 Results and Discussion
### Descriptive Statistics  
The descriptive analysis includes meta-data and categorical and continuous predictors used in ML regression modelling. Firstly, the size of companies consists of large (57), medium (41), and small (13) companies, classified according to NACE into agriculture, forestry and fishing (10), construction (22), manufacturing (51), and mining and quarrying (28). There are a total of 6746 in Number of Machines (NoM), i.e., fluid power systems identified in the sample with a total of \(n\) = 2775 Maintenance Personnel (nMANP), specifically NoM = 60.775 \(\pm\) 78.132, and nMANP = 25.00 \(\pm\) 29.244.  
Considering the HFVG variable, it was difficult to extract a specific category by text mining; however, the most common fluids include viscosity grade VG32, VG46, and VG68, with VG46 being the most commonly used. The most common types of assets and machinery (HMT) utilised include excavators, CNC machines, material handling machines, manipulators, die casting, production lines, hydraulic presses, extruding machines, etc. The most common Hydraulic Fluid (HFT) types are HV 29.14%, HL 19.43% and HM 8.57%, among others. The rest of the asset characteristics are provided in Table 3.  
The maintenance predictors provide the following insights. The types of maintenance practices identified are overlapping where most applied include PM (Preventive Maintenance) in 73.87% of cases, followed by FBM (Failure-Based Maintenance) or Corrective Maintenance in 34.23%, CBM (Condition-Based Maintenance) in 41.44%, PdM (Predictive Maintenance) 10.81%, OM (Opportunity Maintenance) 3.6%, and DM (Design-out Maintenance) 0.9%. The formulation of proposed practices is developed according to [43]. The FAP explains the personnel responsible for performing fault detection and diagnosis, where 49.5% of personnel is outsourced while 50.5% is indoor. The MAP describes the analysis program consisting of companies without any program (8.1%), visual inspections (39.6%), contamination control (12.6%), oil analysis program (22.5%), and full prognostics and health management (17.1%). The CMS predictor describes categories of sensors used to perform condition monitoring--none (19.8%), pressure/flow (21.6%), pressure, flow, and temperature (27.9%), and including those above with additional contamination and vibroacoustic sensors (30.6%). The LCMI is coded as a binary variable explaining the use of specific lubricant condition monitoring instruments (yes--31.5%/no--68.5%). Lastly, the LCML explains the laboratorial analysis performed to support fault detection and diagnosis of the fluid power system (yes--2.7%/no--97.3%). The rest of the continuous predictors (MPPM, FRT, TTOR, and TTOC  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Mean** & **95\%CI\({}_{\text{L}}\)** & **95\%CI\({}_{\text{U}}\)** & **St. dev.** & **Min** & **Max** & **Sum** \\ \hline MOF\_Est & 432.869 & 305.850 & 570.501 & 742.147 & 50.000 & 3500.000 & 48,048.460 \\ HMA\_Est & 10.749 & 9.933 & 11.622 & 4.420 & 2.500 & 35.000 & 1193.170 \\ NWP\_Est & 178.385 & 164.393 & 193.927 & 81.805 & 65.000 & 415.250 & 19,800.740 \\ NWF\_Est & 90.165 & 78.146 & 102.117 & 63.118 & 20.000 & 242.550 & 10,008.290 \\ NWEC\_Est & 29.407 & 25.032 & 34.729 & 26.160 & 2.170 & 113.390 & 3264.170 \\ MPTM & 0.611 & 0.464 & 0.792 & 0.838 & 0.059 & 5.000 & 67.837 \\ FRT\_Est & 1382.40 & 1149.69 & 1657.75 & 1420.89 & 200.0 & 8640.00 & 153,446.49 \\ TTO\_Est & 382.11 & 344.173 & 423.07 & 225.76 & 25.00 & 1000.00 & 42,414.13 \\ TTOC\_Est & 2718.81 & 2096.76 & 3340.86 & 3307.014 & 100.00 & 20,000.00 & 301,787.90 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics of assets relying on fluid power transfer or control.