###### Abstract  
Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs' operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry. digital twins; auto-detection; operational data; cyber-physical; Industry 4.0; production system +
Footnote †: journal: Applied  
with other DTs, learning from other DTs, sharing common knowledge, detecting the anomalies at the edge level, and understanding the overall picture and conditions of the operational manufacturing environments. The DT receives data from wind turbines sensors in real-time, offering the status situation, future failures and detecting under performances. DTs' collaboration of wind turbines reduced maintenance costs by reducing visits to the wind farm and fixed failure. It replaced, predicting component fail time, reasons, fixed and providing failure position. Using DTs collaboration on wind farms, the energy will improve, creating renewable wind energy more affordable and help Industry 4.0 to become sustainable, more intelligent and greener. In Figure 1, we have described the high-level of the automatic detection framework of DTs (i.e., sensors data) using semantic models and operational data (i.e., failure and erroneous data). It is shown that the consuming operational data from DTs is analyzed at edge level based on semantic models, including knowledgebase management [3]. The analysis identifies whether the DT data is a failure or erratic and shows whether the problem is happening locally or happening for all adjacent DTs representing the same device. In case there is a problem that exists for all of the deployed DTs, the system can intelligently react by sending an early notification about the failure to the decision-makers. Otherwise, the system ignores the erroneous data extracted from that DT and then updates it with the correct data to keep the operational data consistent within the production system. In order to guide this research work, we stated these two research questions:  
* (RQ1) _Is it possible to automatically detect DTs (sensors/device) producing failure/erroneous observations at a high level of abstraction within collaborative DTs?_
* (RQ2) _In case the observations are failures, is this failure detected by one DT or by all deployed DTs in the production?_  
To answer these questions, we proposed a conceptual framework for collaborative DTs to auto-detect the erratic operational data to answer these questions. The proposed framework consists of six components: intelligent services, operational data management, knowledgebase management, models, synchronization, and simulation. The inputs will be DTs data which are the observations produced by the sensors within the production  
Figure 1: The high-level of the automatic detection of collaborative digital twins (DTs) using semantic models and operational data (i.e., failure and erroneous data).