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Table 2 Common issue identification techniques

From: An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities

Technique Strategies Description Implementation Weakness
Basic intelligence and reporting Corrective Reporting is used to manually assess if a particular parameter is outside an expected operating boundary. If so, further investigation of the potential issue can be undertaken Existing industrial information systems, such as Manufacturing Execution Systems (MES) or Building Management Systems (BMS), which are used in day-to-day operations, can be used to generate reports Largely manual process, with static and ad hoc issue identification. Also dependent on the ability of the expert analysing the report to observe the anomaly, which may be somewhat subjective and easy to overlook due to human error
Fault detection and diagnosis (FDD) Corrective FDD consists of a set of encoded fault logic (e.g. IF/THEN rules), to identify potential issues based on a set of input data FDD capabilities are embedded in some industrial information systems, but also exist as standalone systems and tools that can be used to monitor specific types of equipment Logic employed is typically specific to equipment, and detection means that the issue is already present and may be impacting operations in some way
Condition-based monitoring (CM) Corrective
CM focuses on monitoring a particular measurement, or set of measurements, to determine if an issue has, or is likely to occur. The condition is fired when the monitored parameter(s) are outside a predefined range CM is available in many modern industrial information systems, and can be viewed as an extension to reporting and monitoring modules, with a condition/trigger that automatically highlights issues The condition is specific to equipment and/or its components. Therefore, performance and accuracy is dependent on the appropriate parameter(s) being chosen, and condition values set
Predictive maintenance (PM) Preventative
PM employs statistical learning techniques to anticipate the occurrence of an issue, and/or estimate the RUL of equipment and components Predictive methods in current industrial information systems are limited, and therefore, PM it is common to see implementations as standalone systems or tools To develop an accurate tool, an appropriate amount of high-quality data must be available to inform the statistical learning model
Prognostics and health management (PHM) Corrective
PHM uses a holistic approach to issue identification, and comprises FDD, CM, and PM, to highlight issues at different stages so that optimal equipment health is maintained Given multiple techniques are used in PHM; it is typically implemented as a dedicated system. In some cases, where interoperability exists, PHM systems may leverage the FDD, CM or PM capability of an existing system Implementing multiple techniques represents challenges – e.g. when should a particular technique be used. This arguably makes PHM more complex than any single technique