# Remote Monitoring and Diagnosis for Building Maintenance Units Based on Internet of Things System
## 2 System Design and Application
### Overall System Architecture Design
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault Type** & **Phenomenon** & **Cause** \\ \hline \multirow{6}{*}{Abnormal self-check} & Host communication abnormality & Communication line disconnected; the communication module is damp. \\ \cline{2-3}  & Inverter fault & Voltage too high; excessive temperature; excessive humidity leads to internal short circuit. \\ \cline{2-3}  & Rope slack fault & Wear and tear of the rope-loosening mechanism. \\ \cline{2-3}  & Overspeed protection failure & Brake disk wear; overspeed detection failure. \\ \cline{2-3}  & Chain breakage & Wear and tear; overload; improper installation leads to unstable splicing. \\ \hline \multirow{2}{*}{No input signal} & Knob contact malfunction & Poor contact of contacts caused by moisture, \\  & & arcing, and dust. \\ \cline{2-3}  & The wiring is disconnected & Loose joints; broken due to external force. \\ \hline \multirow{2}{*}{No output signal} & The device has no action & Equipment action output conditions not met. \\ \cline{2-3}  & Action limit switch failure & Internal short circuit or open circuit caused by water ingress and aging. \\ \hline \multirow{2}{*}{Contactor failure} & Contactor failure & Excessive loads lead to arc breakdown; high humidity causes internal moisture. \\ \hline \multirow{2}{*}{Motor protector failure} & Motor protector failure & Long-term high temperatures lead to aging; excessive humidity on the rooftop leads to a decrease in insulation performance. \\ \hline \multirow{2}{*}{Motor failure} & Motor failure & Unstable voltage, high temperature, and moisture. \\ \hline \multirow{4}{*}{Hydraulic system failure} & Hydraulic valve malfunction & Poor assembly quality and clogged valve core. \\ \cline{2-3}  & Hydraulic pipeline damage & External damage and corrosion. \\ \cline{2-3}  & Hydraulic joint leakage & Damaged seals; improper installation; corrosion; high-voltage impact. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The causes of each fault phenomenon. \[I=\left[ \begin{array}{c}I_{11},I_{12},\cdots,I_{1M}\\ I_{21},I_{22},\cdots,I_{2M}\\ \cdots\\ I_{Z1},I_{Z2},\cdots,I_{ZM}\end{array} \right] \tag{9}\]

The elements of the matrix \(I_{kj}\) are rigorously defined as the posterior probability \(P(f_{k}|s_{j})\), representing the likelihood that the root cause \(f_{k}\) is responsible for the fault phenomenon \(s_{j}\), as shown in Formula (10):

\[I_{kj}=P(f_{k}|s_{j}) \tag{10}\]

This matrix serves as the core of the diagnostic knowledge base, quantifying the inference strength from the symptom \(s_{j}\) to the root cause \(f_{k}\). Parameter estimation for the matrix \(I\) typically involves statistical analysis of a large dataset of historical fault cases. A common approach is to use frequency-based maximum likelihood estimation, as shown in Equation (11):

\[\hat{P}(f_{k}|s_{j})=\eta_{kj}=\frac{N(f_{k},s_{j})}{N(s_{j})} \tag{11}\]

where \(N(f_{k},s_{j})\) represents the number of instances in which phenomenon \(s_{j}\) occurs and is associated with root cause \(f_{k}\), and \(N(s_{j})\) denotes the total number of occurrences of phenomenon \(s_{j}\) in the historical dataset. The validity of this estimation relies on the adequacy and representativeness of the historical data, assuming that the statistical patterns observed remain applicable in the current diagnostic context. During the inference process, for any identified active fault phenomenon \(S_{j}\in S_{active}(t)\), the diagnostic system extracts the corresponding column vector from the probability matrix \(I\) as Formula (12):

\[P_{j}=\left[I_{1j},I_{2j},\ldots,I_{zj}\right]^{\mathrm{T}}=\left[P(f_{1}\,|s_ {j}),P(f_{2}\,|s_{j}),\ldots,P(f_{Z}\,|s_{j})\right]^{\mathrm{T}} \tag{12}\]

Vector \(P_{j}\) provides the posterior probability distribution of all potential root causes \(f_{k}\in F\), given the observed phenomenon \(s_{j}\).