12500 Grand River Road, firstname.lastname@example.orgBrighton, MI 48116
“Ask for a weld shop tune up”
2. Data management.Huge amounts of data can becollected at any given instant bysensors fitted on critical plantequipment. This data can beoverwhelming in its raw form.When switching to a PdM system, it is important that thesystem is capable of filteringthrough and mapping out critical data from all sensors.
The PdM algorithms andpredictive models should beable to predict failures and failure modes from analyzed data.The data-management platformis expected to provide a user-friendly interface, throughwhich complex data is displayedin simple and understandableforms (graphs and pictorials),which can be acted upon to perform proactive maintenance.
PdM systems should be capable of collecting data on critical assets continuously, withoutaffecting plant operation or thetechnical capabilities of the data-management platform.
3. User adoption andtraining. Switching to predictive maintenance means that themaintenance staff will be exposed to new systems, software,and procedures. A PdM systemis bound to disrupt existingmaintenance practices, and it isimportant that staff understandadditional software needs andchanges that arise from its implementation.
Smooth transition to a PdMmaintenance approach will depend on user acceptability andclear understanding of systemsby maintenance teams. Additionally, creating data models that will be used to predictequipment breakdowns is acomplicated task and often willcall for employing additionalstaff, such as data scientists andreliability engineers.
4. Ease of monitoringand alert generation. Before you switch to predictivemaintenance it is critical thatyou adopt a system that is easyto monitor. Apart from breakingdown complex data sets intounderstandable analytics, detection of errors and generation