CIBSE Technical Symposium, Sheffield, UK 25-26 April 2019 Fault …/file/C... · 2019-05-09 · CIBSE Technical Symposium, Sheffield, UK 25-26 April 2019 Page 3 of 17 the visualisation

CIBSE Technical Symposium, Sheffield, UK 25-26 April 2019

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Fault Detection and Localization using IFC: A Case Study of BIM-based Visualisation of BAS-related Faults FATIN ABDOUL-WALI BARCHENG, MSC (DAAD) School of Architecture, University of Sheffield. [email protected] TSUNG-HSIEN WANG BS ARCH, MS ARCH, PHD, FHEA [email protected] CHENGZHI PENG BARCH, DIPLA, MSC, PHD School of Architecture, University of Sheffield. [email protected]

Abstract Based on a topological network for fault detection and localization, this paper proposes a BIM-based visualisation technique for diagnosing BAS (Building Automation Systems) components in terms of forward and backward fault propagation. The premise is twofold: (1) that visualisation of a building can provide a spatial context conducive to human reasoning; (2) that BIM models can be accessed to generate accurate 3D visualisation of spatial-functional relations of building components targeted by a fault detection strategy. Implemented in Python, the visualisation technique is tested on a case study by executing knowledge-based diagnostic rules, retrieving data from the building’s IFC content and generating visualisation of the fault detection and localisation results.

Keywords Fault Detection and Localisation, HVAC System, BIM-based

visualization, IFC semantics, Facility Management

1.0 Introduction There has been a growing interest towards data interoperability between Facility Management (FM) information and Building Information Modelling (BIM) for a better control and enhanced building operation. Prior efforts have been made to facilitate cross-referencing of data between different information management systems in order to optimize the monitoring process and building performance as a whole. However, BIM integration with Building Automation Systems (BAS) still faces limitations associated with fault detection strategies related to building services systems such as HVAC. As different components within a complex HVAC system may manifest similar symptoms for a reported problem, the fault detection process to locate the roots of the problem may be aided by accurate visualisation information (1). Several efforts (2-4) have been made to develop an ontological system to link IFC objects with the FM work by managing BIM-based FM information via the Semantic Web for an enhanced interoperability and accessibility of the FM data. Kim et al. (4) state that IFC-based BIM is used for its ability to systemically and semantically manage building elements throughout its lifecycle. While to establish an interoperability of BIM data with FM system, COBie is used as a data exchange system for the handover process at the beginning of the O&M stage as a data spreadsheet containing external data. Together, they create a framework for the management of FM data that is stored in an external heterogeneous database (DB), by semantically integrating them for an object-oriented approach to FM information that support BIM-based FM works, such as 3D visualization and maintenance work management.

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Some of these studies either did not incorporate the IFC format in its problem solving approach and disregarded its role in enhancing the BIM-FM interoperability, or did not use a BIM-based visualisation for fault detection and localisation. For instance, Golabchi et al. (5) used an as-built BIM model to extract the HVAC distribution system and then store it as a tree network. They proposed a ticketing system and implemented a fault detecting algorithm to locate faults in the network and facilitate the retrieval of relevant information to faulty equipment. This approach to data visualisation is similar to this paper’s attempt to generate a BIM-based visualisation, but in contrast, the authors did not implement a knowledge-based method to fault detection. Following on from that, Delgoshaei et al. (7, 8) have developed a set of ontologies for different domains and a knowledge-based reasoning model that uses Apache Jena and employs Jena rules for an automated fault detection and diagnosis (FDD) in HVAC systems. The authors also contemplated the possibility of the integration of their proposed methodology into BAS by means of data extraction from IFC. In this paper, based on the existing ontologies and inference-based rules we propose an implementation of FDD strategies and development of visualisation frameworks using IFC data and Python coding.

1.2 Problem Statement The traditional management of building facilities and equipment involves a range of repetitive tasks such as inspection, testing and diagnosis, making it one of the most time-consuming and labour intensive process throughout the lifecycle of buildings. Additionally, the identification of the root cause and location of the faulty equipment relied on building automation system (BAS) combined with 2D CAD drawings. Yet, the effectiveness of such method is limited due to the lack of proper interoperability between data sources, e.g. spatial information and building facilities, and the difficulty to read the drawings with overlapping of layered services. Thus, the adoption of BIM as a 3D visualization tool from O&M perspective is favoured, as it also provides further useful functions from design collaboration and fault detection to quantity take offs and schedules. According to both Chan et al. and Kim et al. (4,6), fault detection and localization through BIM-based visualization has proven itself to be efficient and more reliable than 2D CAD drawings which may introduce human error to the links between FM system requirements, and its interdependent on the precision and quality of the data collected from the building information. It also provides accurate data input for FM through the utilization of a computerized data management. Another recent paper by (3) also mentions the lack of model-driven approaches, despite it being an asset, to fault detection applications specially when combined with data-driven approaches. This was attributed to the use of natural languages found in traditional documents that cannot be processed or interpreted by machine systems.

1.3 Objectives and Scope To overcome these limitations, this paper proposes a visualisation framework composed of three types of visualisations and a modified knowledge-based rule set, in order to enhance the fault detection and diagnosis (FDD) strategy of the HVAC systems. The framework depends mainly on an ontologically-enriched IFC files acquired from (9) and a knowledge-based Jena Rules developed by (7) expressing a range of conditions for specific domains (building spaces, mechanical elements, etc.) and is used to produce the visualisations for fault detection applications. The following sections of this paper are structured as follows: 1) A description for the proposed methodology, 2) The case study with two test fault scenarios, 3) The development of


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the visualisation frameworks, and finally, 4) the results, discussion and conclusion to the study.

2.0 Methodology The case study IFC file is a dormitory building obtained from (9), where an ontology-rich BIM dataset is made available for public use and further development. This dataset is used to extract the semantic relationships between the different building components (spaces, HVAC elements, controls), to obtain the primary visualisation framework. This is accomplished through accessing the Ifcopenshell library (10) in Python. The related COBie file containing the attribute data is accessed to identify the associated classifications and properties of the components. Based on the gathered information, an external dataset is created to simulate feedback data obtained from an operating BAS and is populated with missing data from the IFC file such as the associated coils and valves for the air handling unit (AHU) and variable air volume (VAV) unit which are the main elements addressed in this study. A cross-referencing of data based on the IfcSpace “Label” attribute is conducted in order to establish a link between different components in the IFC files in relation to their “ContainedInStructure” property. A visualisation framework is proposed (Figure 1) for the building components using two different libraries in Python. The output yields 3 visualisations:

a) NetworkX Library (11):

1- An overall abstract 2D visualisation demonstrating a topological relationship between HVAC elements.

2- A localised 2D visualisation aiding the identification of faults at a smaller spatial framework.

b) OCC Library (12):

3- A 3D visualisation is obtained from extracting the geometric data from the IFC file for the flow distribution elements.

Figure 1 - A visualisation framework for FDD


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Figure 2 shows a schematic diagram for the AHU and VAV unit obtained from (13). In this study, the AHU is composed of a heating coil, valve that controls the flow of water into the coil through the air handling controller and an air temperature sensor, to measure the discharged air temperature from the AHU to the VAV. While the VAV unit is simplified to compose a damper which controls the air flow rate regulated by the VAV box controller, an air temperature sensor to measure the supplied air temperature to the rooms, and finally, an air flow sensor providing readings for the rate of air entering the VAV unit.

Figure 2 - Schematic diagram of a single duct AHU (left) and VAV box (right)

2.1 Test Case Study In order to examine the suggested visualisation framework for a knowledge-based FDD, the authors investigates an IFC case study containing HVAC distribution flow elements, specifically, AHU and VAV units. We use the East Dormitory Test Files hosted by Prairie Sky Consulting (https://www.prairieskyconsulting.com/testing.htm).

These are two of the main components composing the proposed test problem. The files are scrutinized to extract relevant information to the fault scenario, as well as identifying the required additional inputs. The workflow identified in Kim et al. (4) for extracting spatial information associated with building elements is explored to extract the associated data using Python. This requires a group of functions that can accesses the entities within the various IFC classes to mine the desired data from a list of multiple attributes. The following Table 1 shows a excerpt of the extracted data frame with the main HVAC components from the IFC files. Data from different IFC files were cross-referenced based on the main component’s ‘Tag’ attribute, and grouped by their common shared space based on the ‘Label’ column. This is because the ‘GlobalId’ attribute for the elements differ from one IFC file to another.

Table 1 - Excerpt of the extracted data from the Dormatory IFC file


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A new csv file of external data is created with respect to the AHU and VAV unit and their associated attributes. These inputs include coils, valves and their current operating conditions, along with values [0, 1] being representative of discomfort and comfort respectively (Table 2). Figure 3 shows ontology classes and associated properties.

Table 2 - Excerpt of the data needed for FDD rule application

Figure 3 - Ontology classes and their associated properties


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2.2 Test Fault Scenario We focus on a test fault scenario deduced from (7), and introduce additional data by incorporating a VAV unit into the scenario. Figure 4 shows a schematic diagram of ontology classes and their associated objects and attributes. The existing rules developed by the researchers using the Jena Apache language, illustrates the strategy for FDD and are modified according to the described scenario. This is then implemented using Python coding as a set of “IF-THEN” rules applied to the 2D/3D visualisation framework; in order to highlight the role of fault visualisation in FDD. Table 3 demonstrates a set of hypotheses triggered by the detection of a fault. The two scenarios listed below assumes that the occupant thermal comfort has fallen below the set value, reporting that a room is “too cold”. The temperature sensor reading confirms the fault by comparing the temperature set point for the room and the current temperature. This is deduced by applying the first rule “Rule 1” in the fault detection and diagnosis scenario, initiating an enquiry process inspecting for possible causes. The investigation continues by examining three hypotheses that might be the source for the problem through reasoning and identifying the supporting evidence.

Figure 4 - Ontology classes and their properties

The rules found in Table 3 are applied using the Jena rules provided by Delgoshaei et. Al (7). It identifies the evidence needed to validate the hypotheses made for each domain, which are as the following:

The room temperature sensor is broken.

The VAV unit is malfunctioning.

The AHU unit is malfunctioning.


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Table 3 - State, Fault, Hypotheses and a set of FDD Rules for identifying the cause of occupant thermal discomfort

Hypothesis 1 is cleared out by applying “Rule 2”, which examines if the temperature sensor is broken. As the query returns “False”, the detection carries on to Hypothesis 2. To simplify, all temperature sensors are assumed normal in the scenarios below.

2.2.1 Scenario 1: VAV Unit Fault “Flawed VAV Box” For the scenario outlined in Figure 5 and Figure 6, the VAV unit is examined for defects. “Rule 3” checks if the supplied air temperature coming from the VAV unit is equal to the discharged air temperature coming from the AHU unit. This returns “False” as the supplied air temperature value is lower than expected. “Rule 4” then investigates whether the VAV unit is attempting to control the volume of air into the room by checking if the damper is obstructed, hindering sufficient air flow. The damper status reports “True” value for “open.” “Rule 5” confirms that the current air flow passing through the VAV is equal to the expected value by comparing the values and returning “True”, which again ascertains that the damper is working correctly. This leads to the conclusion that the VAV unit might be faulty on a structural level, where the supplied air is mixed with an external air source, hence the reduction in its temperature.

Class Individual Description FDD Strategy

State Occupant

Thermal Comfort

Binary value (0: discomfort;

1: comfort).

Fault Fault Trigger Occupant report returns a (0) value when the AHU unit is operating.

Hypotheses

Hypothesis 1

The temperature sensor which provides feedback to the AHU unit is broken.

Rule 1: Checks if current room temperature is equal to the set temperature.

Rule 2: Checks if the temperature sensor is broken.

Hypothesis 2

The serving variable air volume unit is malfunctioning.

Rule 3: Checks if the supplied air temperature equals the discharged air temperature.

Rule 4: Checks if the damper status is open.

Rule 5: Checks if the current air flow rate is equal to the expected flow rate.

Hypothesis 3

The serving air handling unit is malfunctioning.

Rule 6: Checks if the coil temperature set point is equal to the coil current temperature.

Rule 7: Checks if the status of the valve associated with the coil is open/closed.


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Figure 5 - Scenario 1 HVAC elements, attributes and knowledge-based rules

2.2.2 Scenario 2: AHU unit Fault “Leaky Valve” The VAV unit is examined for faults and is cleared by the rule set, which takes the investigation further to the AHU unit (Figure 7 and Figure 8). In this scenario, the supplied air temperature serving the room through the VAV unit is equal to the discharged air temperature from the AHU unit. This is checked out by applying “Rule 3”. The damper and flow rate conditions are the same as in the previous scenario, resulting in excluding the 2nd hypotheses. The 3rd hypotheses initiates by examining “Rule 6”. The coil temperature set point value is compared to the current coil temperature value, which returns “False” as the current coil temperature is much lower. The associated valve status is then inspected through “Rule 7”. According to (7), the valve should be shut when the current coil temperature is equal to the coil temperature set point. Otherwise, the valve is leaky. The valve status in this scenario outputs “True” for the status “closed”, although the current value differs from the set point. This indicates a fault in the AHU unit’s valve condition resulting in the reduction of air temperature as it escapes through the leaky valve.

Figure 7 - Scenario 2 HVAC elements, attributes and knowledge-based rules


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Figure 6 - Diagram for the rule execution workflow to scenario 1


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Figure 8 - Diagram for the rule execution workflow to scenario 2


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At the end of these scenarios, the FDD rules evaluate the most probably hypotheses and conclude whether any of them is “True”. The following section discusses the focus of this paper, where the FDD is aided by a visualisation framework that exploits the existing rules and generated results to deliver a more comprehensive visual output for the entire FDD process.

2.3 Visualization Framework Following the FDD strategy, a set of graphs and displays are generated to facilitate fault visualisation and localisation using Python graphical tools. The visualisation framework consist of 2 types of visualisation (abstract and contextual) generating three kinds of visual output:

1. An overall 2D abstract visualisation for the HVAC components contained within the dormitory building. These components are visualised as a topological network graph using the NetworkX library which consists of: a) Nodes: that carry attribute data such as the name/tag/status for the flow element, and, b) Edges: demonstrating the relationships and connectivity between the nodes. The flow elements are distributed within the network based on their real position in the geometric model, by using their central coordination to pin point their location. This creates a topological layering in the 2D visualisation that separates the elements based on their level and location within the building in planar view. Figure 9 shows an example of the elements’ associations with one another.

Figure 9 - HVAC flow elements distributed based on their real position in the geometric model using NetworkX


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2. A level-specific 2D visualisation for the flow elements and their associated objects and spaces. In this visualisation, a more detailed view is provided for the spatial context. Each level contains a range of classifications (spaces, associated HVAC elements, control elements, etc.) that are linked together with similar characteristics to the 1st visualisation (nodes and edges). The nodes are populated with a dictionary of attributes used for the application of fault detection rules in Python. These attributes include: status, current temperature, temperature set point and comfort value. Figure 10 demonstrates an example for the visualisation specific to level 1 before applying any FDD rules. In Figure 11, a fault report is generated in “Bedroom 215” set off rules for identifying the potential elements causing the problem associated with the space. The relating nodes are highlighted with a distinguishing colour and their sizes become larger than the irrelevant nodes. The hypotheses are then tested for each scenario and the final output locating the fault is visualised based on the evidence provided by the IF-THEN rules. For scenario 1 where the VAV box is found to be at fault, Figure 12 shows the specific faulty VAV unit associated to the level and space, and is visualised with a different colour from the rest of the nodes. Similarly, the faulty valve in scenario 2 is visualised according to its relevant evidence.

Figure 10 - Network visualisation specific to level 1 before applying FDD rules


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Figure 11 - Network for level 1 (above) and level 2 (below) with highlighted nodes representing the potential components causing the problem associated with the spatial locations


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Figure 12 - Network showing the specific faulty equipment’s node in different colour after application of the FDD rules. (Scenario 2 above) (Scenario 1 below)


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3. Lastly, a 3D geometric visualisation for the flow elements. This graphical display window is generated using the OCC library in Python and visualises the HVAC flow elements extracted from the IFC file. The purpose of this additional visualisation is to provide a more contextual display for the components rather than abstract. The faulty element is highlighted in a different colour from its neighbouring components after being identified and localised in the abstract network. Figure 13 shows the extracted geometric data for the mechanical elements contained within its corresponding spaces. Below are three display windows: a) visualisation for the components in a fault status equal to “False”, b) and c) visualises the result for scenario 1 and scenario 2, respectively.

Figure 13 - Extracted geometric data for the flow elements contained within its corresponding spaces

It must be noted that the fault detection strategy described takes into consideration the functional characteristics of the nodes (their attributes), the temperature recorded by the spaces and HVAC elements, along with their activity status as parameters to the visualisation system. These parameters are treated as BAS inputs that are in theory constantly changing according to the provided feedback from the operating system. By

a)

b)

c)


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employing this fault detection visualisation framework, it is possible to develop a plan that exploits the structured IFC-ontologies to extract relationships between different objects and improve the fault detection functions performed by the facility management (FM) system. Through the utilization of inferred-based reasoning, irregularities in the operation are identified, localised and showcased for a more thorough comprehension of the generated fault.

3.0 Conclusions and Recommendations Implementing an efficient FM-related application requires a meticulously defined documentation of model-based information, in order to integrate data semantically and manage building functions during the operation and management (O&M) phase. Various initiatives have been directed towards the utilization of BIM-based and knowledge-assisted visualisations to aid in the cognitive reasoning of FM managers for problem solving. This move to data driven visualisation is to improve the strategies undertaken to solve issues associated with the interoperability between FM and BIM in the O&M phase of a building’s lifecycle. Within this context, this paper proposes a visualisation framework to facilitate building facility management activities, namely fault detection and localisation through a BIM-based visualisation and cross-referencing of data with building automation systems (BAS) related inputs.

The IFC data structure is analysed and evaluated through a case study (East Dormitory Test Files, Prairie Sky Consulting), where IFC and inference-based FM data were linked semantically and then visualised using Python coding. A change in status of a certain domain triggers the FDD strategy, and it visualises a chain of effects in order to produce a localised result deduced by the relationships between the domain elements. This produces 2 types of visualisations that provides abstract and contextual views for the fault. The method used in this paper reports a single fault and could be improved by the implementation of fault scenarios that are more complex and have an impact on several equipment. The semantic relationships found between the HVAC distribution elements in an IFC file could be exploited to formulate a model-data-driven approach for fault detection and localisation applications. These data should also be extended with detailed inputs and linked to data stored in FM software applications for a more realistic output.

Acknowledgement The authors thank Rob Jackson, Associate Director, Bond Bryan Digital, for pointing us to the East Dormitory Test Files hosted by Prairie Sky Consulting, which we have used in this study as a test case.

References (1) Motamedi A., Hammad A. and Asen Y. ‘Automation in Construction Knowledge-assisted BIM-based visual analytics for failure root cause detection in facilities management’, Automation in Construction, (2014), Elsevier B.V., 43, pp. 73–83.

(2) Niknam M. and Karshenas S. ‘A shared ontology approach to semantic representation of BIM data’, Automation in Construction, (2017), Elsevier B.V., 80, pp. 22–36.


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(3) Dibowski H., Holub O. and Rojíček J. ‘Ontology-Based Fault Propagation in Building Automation Systems’, International Journal of Simulation: Systems, Science and Technology. Prague, Czech Republic, (2018), pp. 1–14. (4) Kim K., Kim H., Kim Ch., Kim J. and Yu J. ‘Integration of ifc objects and facility management work information using Semantic Web’, Automation in Construction, (2018), Elsevier, 87(December 2017), pp. 173–187. (5) Golabchi A., Akula M., Kamat V. R. ‘Leveraging BIM for Automated Fault Detection in Operational Buildings’, 30th International Symposium on Automation and Robotics in Construction and Mining: Building the Future in Automation and Robotics, in. Montréal, Canada: (IAARC 2013). (6) Chan P. S., Chan H. Y. and Yuen P. H. ‘BIM-enabled streamlined fault localization with system topology, RFID technology and real-time data acquisition interfaces’, IEEE International Conference on Automation Science and Engineering, (2016)–November, pp. 815–820. (7) Delgoshaei P. and Austin, M. ‘Framework for Knowledge-Based Fault Detection and Diagnostics in Multi-Domain Systems : Application to HVAC Systems’. (2017) Maryland. (8) Delgoshaei P., Austin M. A. and Veronica D. ‘Semantic Models and Rule-based Reasoning for Fault Detection and Diagnostics : Applications in Heating, Ventilating and Air Conditioning Systems’, in. Venice, Italy: The Twelfth International Conference on Systems (ICONS), (2017), pp. 48–53. (9) Software Testing, East Dormitory Project Files by Prairie Sky Consulting, licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. 16.10.2018. Available at: https://www.prairieskyconsulting.com/testing.htm (10) IfcOpenShell Home Page and Academy, 16.10.2018, Available at: http://ifcopenshell.org/ and http://academy.ifcopenshell.org/ (11) NetworkX Documentation, 09.11.2018 Available at: https://networkx.github.io/documentation/networkx-1.10/index.html (12) PythonOCC, 16.10.2018, 3D CAD for Python, Available at: http://www.pythonocc.org/ (13) Schein J., Bushby S. T. and Castro, N. S. ‘Results from Field Testing of Air Handling Unit and Variable Air Volume Box Fault Detection Tools’. NISTIR 6994. Boulder, Colorado. (2003). Available at: https://www.nist.gov/publications/results-field-testing-air-handling-unit-and-variable-air-volume-box-fault-detection.

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CIBSE Technical Symposium, Sheffield, UK 25-26 April 2019 Fault …/file/C... · 2019-05-09 · CIBSE Technical Symposium, Sheffield, UK 25-26 April 2019 Page 3 of 17 the visualisation