Fault Detection and Diagnosis for Smart Buildings: State of the Art, Trends and Challenges

Sanja Lazarova-Molnar1, Hamid Reza Shaker1, Nader Mohamed2, and Bo Nørregaard Jørgensen1 1 Center for Energy Informatics, University of Southern Denmark, Denmark

2 Middleware Technologies Labs., Bahrain slmo@mmmi.sdu.dk, hrsh@mmmi.sdu.dk, nader@middleware-tech.net, bnj@mmmi.sdu.dk

Abstract— Worldwide, buildings account for ca. 40% of the total energy consumption and ca. 20% of the total CO2 emissions. While most of the energy goes into primary building use, a significant amount of energy is wasted due to malfunctioning building system equipment and wrongly configured Building Management Systems (BMS). For example, wrongly configured setpoints or building equipment, or misplaced sensors and actuators, can contribute to deviations of the real energy consumption from the predicted one. Our paper is motivated by these posed challenges and aims at pinpointing the types of problems in the BMS components that can affect the energy efficiency of a building, as well as review the methods that can be utilized for their discovery and diagnosis. The goal of the paper is to highlight the challenges that lie in this problem domain, as well as provide a strategy how to counterfeit them.

Keywords—smart buildings; diagnosis; review; challenges; strategy

I. INTRODUCTION Rigorous building standards and the global push toward

sustainability have increased the trend toward smart buildings. Smart buildings [1], as typically defined throughout literature, represent buildings that have been built-in building intelligence and/or retrofitted to reduce their energy consumption and CO2 emissions without compromising occupants’ comfort [2]. Buildings, typically, can become smarter by two means: 1) by using intelligent Information and Communication Technology (ICT) solutions, or 2) by retrofitting the building, using superior materials or constructional solutions. Typically, to improve and better meet preset performance goals, a combination of both approaches is deployed. In one of our previous works, to determine the optimal combination of both approaches, we highlight the necessity of affording assessable solutions for smart buildings, as currently it is very challenging to forecast the impact of any of these prospective building enhancements [3]. A substantial amount of new technology is being deployed in developing more intelligent buildings, as well as new and superior materials, which have, however, not yet stood the test of time. These novelties, however, also increase the probabilities of subsystems and components failing, at times with hard to predict outcomes and aftereffects. Finally, this implies that the new buildings’ solutions represent both a challenge and an opportunity to reduce energy-related costs.

As buildings make up for the largest portion of the total energy use worldwide, and malfunctioning of building energy equipment can waste between 15% and 30% of energy [4], timely diagnosing and intervening in the case of faults is highly critical. Therefore, if failures are not handled adequately in a timely and cost efficient manner, they may lead to excessive and wasteful energy use. This is the reason why Building Management System (BMS) fault detection and diagnosis has lately gained in importance. Here we would like to note that user behavior is also a significant contributor to the anomalies in the energy performance. However, in this writing we focus on the building equipment, which is typically the first layer. Once faults in building equipment has been ruled out as potential causes of anomalies, the next step is to analyze occupant behavior.

In this paper we review the latest research in the area of Fault Detection and Diagnosis (FDD) for buildings, with the goal of identifying the existing gaps. Furthermore, we review and analyze the types of faults that could occur in a BMS, as well as general FDD methods, in an attempt to discover potential matches. Finally, we identify the major challenges that lie in this problem domain and propose a strategy of how to tackle them and close the existing gaps.

II. FAULT DETECTION AND DIAGNOSTICS Engineering systems ranging from a simple component to a

whole complex system are vulnerable to different kinds of faults. Faults may cause sub-optimal operation and decline in performance if not even preventing the whole system from functioning. It is therefore important to detect faults quickly, and to identify their causes, severity, and consequences. FDD methods are proposed to address these issues. FDD have received a lot of attention over the last few decades. In this section we present a short overview of the FDD methods and their classification. This survey is followed by a brief discussion of the advantages and limitations of the FDD methods.

A. Overview of general FDD methods In the literature on FDD methods, the system is defined as

faulty when at least one of characteristic features or parameters of the system changes from a healthy situation. It is clear that in a healthy system all characteristic features and parameters are in an acceptable ordinary and standard form [5-7].

FDD methods can be divided into the following five broad categories: Model-Based FDD methods, Signal-Based FDD methods, Knowledge-Based FDD methods, Active FDD methods and Hybrid FDD methods. In the following, these methods are briefly described.

1) Model-Based FDD methods Model-based FDD was first proposed by Beard [8] in 1971.

Suitable modeling is a prerequisite for a successful FDD by Model-based FDD methods. Suitable models for FDD are usually obtained by first principles modeling or systems identification methods. The models are used to predict the output of the healthy system. Algorithms within this category of FDD methods constantly monitor the output of the system and check if it deviates from the prediction and in such a way they detect a fault.

Model-based FDD methods can be divided into four categories depending on the class of models they are developed for [6]: Deterministic Model-Based FDD Methods, Stochastic Model-Based FDD Methods, Model-Based FDD methods for Discrete-events and Hybrid systems, Model-Based FDD methods for Discrete-events and Hybrid systems, Networked and Distributed Systems.

2) Signal-Based FDD methods Signal-based FDD methods mainly use signals, which are

obtained from measurements for diagnostics [9-12]. The algorithms within this category derive symptoms of a healthy system as an output of the symptom analysis and the knowledge of the system, which are at disposal. When a system is faulty, symptoms that appear in the measured signal differ from those of healthy systems. Typically, these methods analyse signals in either time-domain or frequency-domain. However, there are also methods in this category which use both time and frequency domain.

3) Knowledge-Based FDD methods Knowledge-Based FDD methods require sufficient amount

of historic data. These methods use methods from artificial intelligence to extract the knowledge based from the historic data reflecting the relationship between system variables. The behaviour of the system is monitored in real-time and is compared with the knowledge base to detect possible deviations and make fault diagnosis decisions. Depending on the knowledge extraction process in this category, the methods could be divided into qualitative methods e.g. [15-18] and quantitative methods e.g. [19-21]. Some of the most popular Qualitative Knowledge-Based FDD methods are those that are based on expert systems. This approach basically evaluates real-time data according to a set of rules, which are derived from the knowledge of an expert human operator. Quantitative knowledge-based methods use pattern recognition for diagnostics.

4) Active FDD methods Active FDD methods are methods that evaluate the

behaviour under suitable input test signals for FDD [22-25]. Injecting test signals in active FDD increases fault detectability, which is an important advantage of active FDD.

The added input test signals may compromise the performance and therefore their effect should be minimized.

B. Advantages and Limitations of FDD methods The FDD methods, which have been presented, have their

advantages and also limitations. Model-based FDD methods are capable of detecting and diagnosing unknown faults. This is an advantage of these methods, in particular, because they do not need a large amount of real-time data for this. Model-based FDD methods, however, require models, which could describe the input-output relationship accurately. Such models might not be available or they might be difficult to derive. Signal-based and knowledge-based FDD methods do not need a complete model, which is an advantage.

However, for FDD, signal-based methods generally use the features of the output signals without using much information of the inputs. This reduces the performance of FDD, in particular, when an unknown disturbance exists in the system. The knowledge-based FDD methods require a large set of historical data and consequently impose high computational burden. These FDD methods might not be able to handle unknown faults effectively. The artificial test input is the drawback of in active FDD methods, while their high fault detection capability is their main advantage.

In the following section we focus on the types of faults and FDD approaches in buildings.

III. FAULT DETECTION AND DIAGNOSTICS IN BUILDINGS

A. Faults in Buildings It is difficult to obtain detailed information on energy consumption in buildings, since it requires more detailed monitoring and measurements than what is usually available. However, the energy consumption yearly report [13] shows how the total energy consumption can be divided into different end-uses in commercial buildings (Fig. 1). The statistics show that the energy end-use of commercial building are as follows: lighting 20.2%, spacing heating 16.0%, spacing cooling 14.5%, ventilation 9.1%, refrigeration 6.6%, other end-uses 33.6% [13, 14]. The faults in the system can occur in connection with each of these end-uses. This figure reveals how costly a fault could be in terms of its energy use. Studies show that 25%–45% of energy HVAC energy consumptions are wasted due to faults [26].

Fig. 1. Commercial Energy End-Use Spilt [13, 14]

A thorough investigation by Roth [27] concluded that typical faults in commercial buildings consist of 13 types of faults. The annual impact of each of them in terms of energy consumption is presented in Table 1. Further investigation shows that faults due to the wrong configuration are also a typical fault in new buildings, which has not received an adequate level of attention [28, 29]. Examples of such faults are:

1) Wrongly configured building equipment, where the setting of the equipment is wrong.

2) Misplaced or wrongly wired sensors and actuators.

In the following we provide an overview of the FDD methods aimed specifically at buildings.

B. Smart Buildings FDD Fault Discovery and Diagnostics (FDD) is an important

matter in smart buildings. Operation cost of buildings has been shown to be significantly reduced by utilizing automated FDD [4]. These statistics have triggered a significant amount of research in the field of FDD for buildings. We review the most significant buildings FDD approaches as follows.

FDD approaches for buildings have been exhaustively studied and summarized in [4] and, subsequently, in [30]. Even though the presented work is not very recent, it is still the most comprehensive overview of FDD methods for building systems that features their comprehensive classification. In this overview, FDD methods are classified into: Qualitative Model-Based, Quantitative Model-Based, and Process History Based methods. Qualitative model-based methods are used when it is difficult to generate accurate quantitative models, or when decisions depend on elements that cannot be quantified. They work with qualitative knowledge, expressed in terms of intervals or symbols, and developed models describe relations among these qualitative

parameters. Quantitative model-based methods are typically based on analytical models, developed from physics principles, which is their main asset. Their complexity is, however, the price for this. For this reason, they are often estimated as intractable and usually it is resorted to simplified models for FDD purposes, most typically to linear analytical models. Machine learning algorithms are usually the basis of Process History Based methods. Their accuracy, as usual for machine learning methods, depends to a high degree on the quality of collected data.

In a later, and not as thorough, review of buildings FDD methods implemented in tools [31], the following principle difficulties have been identified:

• Inability to acquire pertinent information

• Scalability and portability of FDD solutions

• Lack of knowledge about the information produced by building automation systems

• Cost and simplicity of creating building models

Finally, generalized at a high level, we can conclude that FDD methods for buildings can be classified in three main classes: Model- and Data-driven methods, and Hybrid methods, that feature methods that represent a combination of model- and data-driven approaches.

Data-driven FDD methods derive the relationships and predictive models based on historical and ongoing data collection. Model-driven FDD methods for buildings are developed solely on basis of physical models, where relations are strictly quantitatively described. Hybrid FDD methods combine elements from both data-driven and model-driven methods. In the following we feature some of the more relevant and recent methods in all three classes.

TABLE I. THE ANNUAL IMPACT OF FAULTS IN TERMS OF ENERGY CONSUMPTION ED [14, 27]

Data driven FDD methods rely on and utilize the data collected from sensors and meters, which is then further processed and deployed by, typically, machine learning algorithms. As example approaches in this class of methods, recently an approach that utilizes quantitative association rules mining and genetic algorithms has been presented in [32]. In this work Fan et al. describe a framework for knowledge discovery in BAS (Building Automation System) data for the purpose of diagnosis that has been implemented for analyzing real data on a case study building. In another example of a data-driven FDD approach, in [33], Namburu et al. present a an approach based on genetic algorithms for fault detection and optimal sensor selection for HVAC chillers. To diagnose faults, the approach is further combined with principal component analysis, support vector machines, and partial least squares.

Model-based FDD methods commonly rely on sound analytical models, derived from physics principles that precisely define the relations of explanatory and response variables in subsystems. These approaches, typically, enforce a number of simplifying assumptions on the model, deterministic behavior being typically one of them. One model-based approach, an implemented and deployed full model-based real-time energy performance monitoring and diagnosis system, is presented in [34]. Saving opportunities, as identified by the system, are at the level of ca. 30% of energy cost. Nonetheless, the presented system has also encountered a number of weaknesses; one of the more significant ones being that the model calibration effort was substantial.

A combination of both data- and model-based approaches is presented in the work by Du et al. [35]. This is an example for a hybrid approach to FDD. In this work, neural network and wavelet analysis are being deployed. The approach focuses on FDD for temperature sensors, flow rate sensors and sensors for pressure in variable air volume (VAV) systems. Furthermore, in this work Du et al. claim that the blend of the two approaches has significantly improved the accuracy, over each one independently. Another hybrid method has been developed for fault detection in air handling units [36]. The presented approach is a blend of Principal Component Analysis (PCA) and wavelet transform (as pre-processing step to PCA). Furthermore, the approach has been compare to the conventional PCA-method and was found to be more accurate.

The typical downside of these methods is that they are still inferior for unknown faults. This, however, is a significant shortcoming, as new equipment for smart buildings emerges almost daily; therefore all types of faults that can occur cannot be predicted and a priori described. From our literature investigation, only rarely unknown faults have been tackled, such as in [37], where authors combine neural networks and clustering analysis which, as they claim, can also detect unknown faults. This is an issue that needs a significant attention and it will be also captured in the framework that we present in Section IV.

Furthermore, the involvement and feedback of occupants also represents a significant aspect of FDD of buildings that cannot be ignored, and needs to be taken into account for the data collection processes. Occupants’ feedback can provide a profound hint to the diagnostics enigma. We factor the impact that occupants (also typically employees) have on commercial building’s performance and identify the opportunities that they carry in one of our previous works [38]. Furthermore, in one of the works on this topic [39] the authors explain how diagnosing performance problems can be supported by an occupants’ complaints handling process. In this work, complaint is referred to as a “statement that a condition is unsatisfactory or could be improved”. Finally, it is definitely reassuring to consider occupants’ feedback data in the FDD processes after studying the results of this study that validate the associations between the occupants’ comfort and building’s performance. These results provide evidence that occupants’ feedback has the potential to enhance the accuracy and provide another perspective of buildings’ diagnostics processes.

As follows, in Section IV we present the outcomes of our review and characterize the gaps and challenges in the domain of FDD for smart buildings. Furthermore, we present a strategy of how to approach this problem from the perspective of data collection and processing processes.

IV. FRAMEWORK/CHALLENGES FOR FDD FOR SMART BUILDINGS

In spite of the vast amount of research that has been accomplished in the area of FDD for buildings, the fact that there are new smart building solutions emerging frequently requires more holistic and portable solutions that would discover and diagnose new and unseen types of faults that would occur with the new retrofitting solutions. In addition to this, we have discovered that very little has been reported in literature on the diagnosing of errors associated with the following problems:

1) Wrongly programmed building management system; i.e. wrongly programmed control logic.

2) Wrongly configured or conflicting setpoints.

3) Wrongly configured building equipment.

4) Misplaced or wrongly wired sensors and actuators.

Furthermore, another gap that we have identified is related to the explanation process in diagnosis. It is important, besides diagnosing the source of the error, to also explain why it has happened as that could enhance future diagnosis and repair processes. Besides, it could also be used to prevent the failure from happening in the future, i.e. in prognostics. Furthermore, it is also in the human nature to want to know not only what has caused an anomaly, but also why it has happened. This can have additional positive impact on the expertise of the involved maintenance employees. It could also serve as a motivation for their support in the data collection processes. Therefore, we intend to work towards a solution to formalize explanations for

possible causes, and provide a way in which that could be shared with building management personnel.

Another challenge that we have identified is the availability of data for diagnosis. This is especially true if we talk about open data. Data collection for data-driven FDD is far from trivial [40], and an optimal one would require a combination of different methods (where almost certainly a significant part will be intrusive) [41]. Therefore, sharing of data where data security is ensured is more than necessary for joining the efforts in this problem area.

Occurrences of combined faults have also not been given the adequate span of attention. Combined faults mean simultaneous (or almost simultaneous) occurrence of different faults. Apparently, in other domains, combined faults are considered a big challenge [42]. However, so far they have not been considered for BEMS, and we expect them to pose a new perspective in the FDD for buildings.

To counterfeit the gaps that we have identified, in the following we present our framework for tackling the FDD in smart buildings. It is based on the following observations:

1. Lack of data (or inferior quality of data) problems

2. Hybrid approaches have yielded better results

3. Collecting occupants’ feedback can provide new dimension to the data collected through meters and sensors

4. Lack of explanations has not been addressed (important for closing the communication loop)

In line with this, in Figure 2, we provide a high-level overview of the important data collection processes and data processing processes for the purpose of diagnosing faults. We distinguish two data collection processes, one that takes place during the first phase of a building operation, whose objective is to train and fine-tune models, as well as derive production and association rules about the operation of the building. The

second process takes place as soon as sufficient data has been collected for the model-building objective. The built models are then used to predict performance of a building, and the derived rules aid both processes of fault detection and fault isolation. Once a fault has been detected and successfully isolated, this information is used to further improve the derived models and rules. All of the data that is being collected is treated as historical once its time stamp is past the current time, thus, data is constantly being used to further improve and fine-tune models.

In the following Section V, we introduce the new technology trends and provide an insight of how they could shape the future diagnostics of buildings.

V. FUTURE TRENDS Several emerging technologies, such as the internet of

things, computer vision, laser scanning, smartphones, virtual sensors, and cloud computing can provide useful mechanisms for enhancing diagnostics processes of energy consumption in smart buildings. These technologies help enable integration flexibility of using different devices, provide better measurements, and offer processing capability for FDD in smart buildings. In this section, we discuss these technologies and how they can be utilized for enhancing the diagnostics of energy consumption in smart buildings.

One of the latest technologies, the Internet of Things (IoT) [43], can be utilized to enable integration of various devices in the diagnostics processes of smart buildings. The IoT aims to interconnect our everyday life devices such as sensors, computers, smartphones, thermostats and actuators [44]. It provides them with information processing capabilities to enable computers to sense, integrate, present, and react to all aspects of the physical world. The IoT can enable plug-n-play capability for FDD systems. This facilitates fast deployment of different devices for FDD systems in a cost-effective manner, thus enabling deployment of temporary systems with extra devices to conduct intermittent inspections or to repair prevailing obvious problems in buildings [45].

There are a number of further new technologies that have great potential to provide or enhance measurements in smart buildings. One of these technologies is 3D geometrical models, produced by computer vision and laser scanning methods. These methods can be used as basis of energy modeling purposes to facilitate diagnostics and measurement of thermal and other environmental conditions [46]. This type of measurements can provide accurate information to reduce energy consumption of buildings.

Data collection processes for buildings FDD can be enhanced to a large extent by the use of smartphones (or personal mobile devices, in general). In general, along with tracking users/occupants mobility, smartphones provide sensing data. Smartphones are usually equipped with multiple sensors, such as light sensors and thermometer, as well as processing and transmission abilities that could communicate current temperature, lighting, and occupancy levels. This

Fig. 2. Data Collection Framework for FDD of Smart Buildings

collection of sensing data is known as participatory sensing [47]. Participatory sensing refers to a sensing where humans passively or actively voluntarily participate in the data collection about the environment, weather, and/or mobility, as well as any other sensory information. It is viewed as a new and promising trend in the data collection processes. This type of sensing is also referred to as people-centric sensing [48] as people with their movement and personal devices play an important role in collecting sensory information. Diagnostic processes in smart buildings can hugely benefit from this collected data, as it can provide insightful hints at no additional cost. There is however, a significant challenge in this data collection, namely, the privacy of mobile device users. However, this is an issue that has been gaining a lot of attention lately, and is being addressed through various platforms.

Smartwatches are new mobile devices that can be utilized to gather useful data [49]. They are wearable computers that can accomplish multiple functionalities. Just like smartphones, smartwatches can be equipped with a range of cameras and sensors. Furthermore, smartwatches can also communicate with other devices through Wi-Fi and Bluetooth. The fact that smartwatches are continuously worn is a great advantage over smartphones.

Virtual sensors [50] are another new technology that can enhance measurements in smart buildings. They are seen as a cost-effective alternative to unpractical and expensive physical sensors. Virtual sensing, also known as soft or proxy sensing utilizes information available from other measurements to calculate an estimate of the quantity of interest. There are two types of approaches of virtual sensing techniques: analytical virtual sensing and empirical virtual sensing [51]. Analytical virtual sensing techniques are based on computation of the measurement estimate using “first principles” models. Thus, they can also calculate calculation of physically immeasurable quantities. The empirical virtual sensing approach is based on the computations of the measurement estimate using available historical measurement data of the same quantity, and its correlation with other available measurements and parameters. Virtual sensors can offer cost-effective sensing capabilities, and are especially necessary in obtaining immeasurable quantities.

The amount of data that gets collected for FDD of smart buildings is Big Data, as it fulfills all criteria, both in terms of velocity, variety and volume. This data will necessitate huge amounts of resources for analysis, storing and processing, as well as advanced software that implements innovative algorithms for accurate FDD, which in turn can be very expensive for building owners. Cloud computing offers a scalable and cost-effective platform [52]. Cloud computing is an Internet-based computing platform that enables and provides shared processing resources and data to computers and other devices on demand. Cloud service providers can provide a number of various services to smart buildings, ranging from data collection and storage services, data processing services, and FDD services. The cloud service provider can be further utilized to collect additional data from a

set of smart buildings, which can further enhance the fault detection and diagnostic processes, especially those that rely on peer benchmarking [53]. In addition, this will enable cloud service providers to implement automated knowledge systems for smart building diagnostics [32] and other advanced mechanisms such as fault detection analysis for the benefit of the clients, the smart buildings.

VI. SUMMARY AND OUTLOOK Accurate diagnosis of problems in buildings presents a

significant building operation cost saving opportunity. With the emerging of new retrofitting and building intelligence solutions, diagnosis has become significantly more complex and, thus, requires new approaches to be designed, as well as further enhancement of the existing ones.

We have summarized the efforts in the area of fault detection and diagnostics for smart buildings with the aim of identifying the gaps and challenges that have not yet been given an adequate span of attention. This also highlights the scope of our research and has yielded an initial framework for addressing these issues. Furthermore, we have also investigated the new technologies and the manners in which they could be utilized to enhance the FDD processes.

ACKNOWLEDGEMENT This work is supported by the Innovation Fund Denmark

for the project COORDICY.

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