Predictive Fault Diagnostics for Connected Building Equipment

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Solution Overview

Problem

Building management systems (BMS) face challenges in accurately predicting and diagnosing faults in equipment, leading to potential equipment failures and increased maintenance costs, as existing methods rely on rule-based systems that are cumbersome and inefficient in adapting to changing conditions.

Innovation Solution

A predictive diagnostics system within the BMS uses supervised and unsupervised machine learning techniques to generate a fault prediction model by analyzing monitored variables, separating normal and faulty conditions, and identifying sub-patterns associated with specific faults, enabling early fault detection and preventative measures.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If rule-based systems are used for fault detection, then the system structure is simple, but the adaptability to changing conditions deteriorates

Engineering Contradiction:

Improvesystem structureVSAvoidadaptability to changing conditions

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The system employs machine learning models that automatically learn and adapt fault patterns from historical data without requiring manual rule updates. The predictive diagnostics system continuously improves its detection capabilities by processing new data, enabling the system to serve itself in adapting to changing equipment conditions while maintaining a relatively simple overall architecture.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system transitions from fixed rule-based parameters to dynamic parameters derived from machine learning models. By changing the nature of detection parameters from static rules to adaptive statistical models, the system achieves both simplicity in implementation and high adaptability to varying equipment states and fault patterns.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If machine learning techniques are used to improve fault prediction accuracy, then the measurement precision of fault detection is improved, but the device complexity increases

Engineering Contradiction:

Improvefault prediction accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments the complex machine learning task into distinct components: a training phase that processes historical data to build models, and an inference phase that applies trained models to real-time data. This segmentation allows the use of sophisticated algorithms without overwhelming system complexity, as each component has a specific, manageable function.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary training of machine learning models using historical fault data before deployment. By pre-training models offline with comprehensive data, the system achieves high prediction accuracy while keeping the runtime system simple, as the complex learning process occurs beforehand rather than during operation.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If proactive fault detection is implemented, then the reliability of equipment operation is improved, but the loss of time for data processing increases

Engineering Contradiction:

Improveequipment operation reliabilityVSAvoiddata processing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system implements periodic model training and updating cycles rather than continuous processing. By training models at scheduled intervals using accumulated data, the system maintains high prediction reliability while avoiding constant processing overhead. The periodic nature allows batch processing of data, reducing overall time loss compared to continuous analysis.

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The system applies machine learning techniques selectively to critical equipment and parameters rather than processing all data uniformly. By focusing computational resources on the most important fault indicators and high-risk equipment, the system achieves significant reliability improvement with minimal data processing time investment.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11835945B2Predictive diagnostics system with fault detector for preventative maintenance of connected equipment

Publication Date: 2023.12.05 TYCO FIRE & SECURITY GMBH

AI summary

A building management system includes connected equipment configured to measure a plurality of monitored variables and a predictive diagnostics system configured to receive the monitored variables from the connected equipment; generate a probability distribution of the plurality of monitored variables; determine a boundary for the probability distribution using a supervised machine learning technique to separate normal conditions from faulty conditions indicated by the plurality of monitored variables; separate the faulty conditions into sub-patterns using an unsupervised machine learning technique to generate a fault prediction model, each sub-pattern corresponding with a fault, and each fault associated with a fault diagnosis; receive a current set of the monitored variables from the connected equipment; determine whether the current set of monitored variables correspond with one of the sub-patterns of the fault prediction model to facilitate predicting whether a corresponding fault will occur; and determining the fault diagnosis associated with the predicted fault.