# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
## 6 Predictive Maintenance Models
### Condition-Based Maintenance  
Condition-based maintenance (CBM) is a management philosophy in which repair or replacement decisions are made based on the current or future condition of equipment [57]. It is defined as a maintenance program that recommends maintenance solutions based on information gathered from condition monitoring and consists of three main steps: data collection, data processing, and maintenance decision-making [58]. CBM is an extended version of predictive maintenance where automatic alarms are activated before a failure occurs. Automatically triggered alarms are activated before any failure is obtained. Complex multi-variable methods and algorithms are used to satisfy the application requirements of CBM [59]. The objective of CBM is to minimize the overall cost of inspections and repairs by collecting and interpreting intermittent or continuous data about the operating status of critical components [60]. CBM can take many forms, from simple to complex. However, the most common form adopted by most industries is the one shown in Figure 9 where the focus is on data collection, prediction, and correction with varying degrees of automation [57]. The starting point is to monitor a set of indicators representative of the health of the equipment over time. When one or more of these indicators reach a predetermined level of deterioration, maintenance actions are taken to return the equipment to the desired state [61]. This means that equipment is only taken out of service when there is direct evidence of deterioration [62]. Figure 9: The seven modules of a conditional maintenance system [57]. The modules of a conditional maintenance system can be presented as follows:  
Module 1: The sensor module provides the CBM system with digitized data from the sensor or transducer. Module 2: The signal processing module receives signals and data from the sensor module or other signal processing modules. The output of the signal processing module includes digitally filtered sensor data, frequency spectra, virtual sensor signals, and other CBM features. The signal processing module may consist of an AI-ESTATE (Artificial Intelligence and Expert System Tie to Automatic Test Equipment). Module 3: The status monitor receives data from the sensor module, the signal processing module, and other status monitors. Its main purpose is to compare the data with the expected values. The status monitor must also be able to generate alerts based on predefined operational limits. This function can be very useful for rapid fault development. Module 4: This module receives data from various status monitors or other health assessment modules. The main purpose of the health assessment module is to determine if the health status of the monitored component, subsystem, or system has degraded. The health assessment module is able to generate diagnostic records and propose failure possibilities. The diagnosis is based on trends in health status history, operational status, and loading and maintenance history. Module 5: The prognostic module has the ability to take into account data from all previous layers. The main objective of the prognosis module is to calculate the future health status of an asset, taking into account future usage profiles. The module should indicate the future health status at a given time or the remaining useful life (RUL). Module 6: The decision support module receives data from the health assessment module and the prognosis module. Its main purpose is to generate recommended actions and alternatives. The actions can be in the area of maintenance, but also in the area of operating the asset until the current mission is completed without a failure occurring. Module 7: The presentation module should present the data from all the previous modules. The most important layers to present are, of course, the data from the health assessment, prognosis, and decision support modules, as well as the alerts generated by the status monitors. However, the ability to lock in even further down the layer should be a possibility.