# PrimaVera: Synergising Predictive Maintenance
## 2 State of the Art
### Data Acquisition  
Predictive maintenance or condition monitoring that goes beyond the visual inspections by a human inspector is always data-driven. The most rudimentary form of data-driven maintenance would be the analyses of log files and error messages [7]. A next level is using specific sensing methods, e.g., vibrational frequency measurements at bearings [8], to assess the health of components. More advanced methods include real-time monitoring which can raise an alarm based on predefined criteria. Taking it further, machine learning and big data analysis of sensor data are being researched [9]. In practice, many companies struggle to incorporate data-driven workflows within their company [4; 5]. First of all, there may be no data, incomplete data, erroneous data, unaligned data or simply not enough data. Logging of errors may be incomplete or data are not stored at all. To apply the power of machine learning techniques to identify patterns, large amounts of data are needed, which means longer periods of time need to be measured. Secondly, data ownership is an issue. Even though machines log data, there is no access to the data, data are too costly or is owned by another company [10]. Thirdly, data are not labelled [10] or is labelled inconsistently. Operators use different nomenclature or report all malfunctions under the same generic error code. That makes it difficult to learn within a single company, let alone to learn from (the data of) 'peer' companies. What is needed is a structured approach to gather data in a goal-oriented way. The cross-industry standard process for data mining (CRISP-DM) [11] is a good starting point, but it describes common approaches. Therefore, specialisation towards specific predictive maintenance problems or domains may be valuable. Given an objective, knowing what to measure is a challenge, knowing how to measure is even more challenging. To overcome this challenge decision support tools which aid in the selection of an optimal sensor strategy can be used. For instance, optimal sensor placement can be found by genetic algorithms [12] or by using finite element models [13]. Sensor costs is another factor which can be optimised [14]. Other elements an optimal sensing strategy decision support tool should take into account are costs of asset/component replacement, expected faults and required accuracy of the condition monitoring system. Another factor the decision support tool should take into account is the goal of monitoring, for instance wear or fatigue. To our knowledge no research has been done into designing an overarching and unifying optimal sensing strategy decision support tool. Moreover, novel measuring technologies may appear which we are not even aware of. For instance, sensors on train axle boxes to enable the monitoring of insulated rail junctions [15]. Or mobile phones of commuters could be used, so-called participatory sensing [16]. For instance, mobile phones have been successfully used to monitor road conditions [17]. So new sensing technologies may show up for predictive maintenance as well. We see three main opportunities, i.e., challenges for data acquisition in predictive maintenance. First, the use of sensor fusion, in particular the combination of remote sensing (lidar, satellite, radar, sound-arrays, etc.) with in-situ sensors [18]. Sensor fusion may compensate for data gaps of a single sensor, leading to new sensing approaches by combining sensors at different distances. Second, the automatic context detection of sensor data. For instance, a bridge is supposed to expand during hot weather, but if the same expansion happens during cold weather this could indicate an anomaly. The challenge is to automatically detect the context (situational awareness). Third, methods for effective cross-company data interoperability are lacking [19]. These methods make it possible to enlarge data sets to get the size needed for machine learning.