**3. Research methodology**

The power distribution system (PDS) of data center has exanimated as case studies for this research. They are 4 topology prototypes of Uptime (Tier I, Tier II, Tier III, and IV) and 5 topology prototypes of BICSI (Class F0, F1, F2, F3, F4) of demonstration on operations and maintenance management. Plan-Do-Check-Act (PDCA) has been applied through PPM model. This process has established more data collection from earlier cycles as the same time this process has certified data training for fault diagnostics and prognostics. The fault diagnostics perform through auto-discovery in DCIM software. StruxureWare software [15] had deployed as auto-discovery subject to ability to detect a device, model it and measure that relevant data points of that equipment. PPM approach has examined by system flow diagram (SFD), as depicted in **Figure 8**.

The SFD begins with data collection from sensing devices at condition monitoring state; data processing and data analytic; feature selection to form statistic modeling before pass through fault diagnostics and prognostics. Output of prognostic process constructs data set and transfers to estimate RUL for input data for predictive maintenance [16]. Predictive maintenance and CBM are synchronized processing with the

For reinforcement of system reliability, the Class F4 and Class F3 are designed for system reliability of PDS for 2(N + 1) and 2 N or N + 1 topology respectively that help more robust on CBM for tolerant maintaining operations with minimal down-

Second, how deep to understand consequence of device/system protection of power distribution system. The failure mitigation map illustrates, for each primary

time effect to entire system.

*Operations Management - Emerging Trend in the Digital Era*

**Figure 7.**

**42**

*Zone preventive approach for CBM.*

**Figure 8.**

*PPM system flow diagram of data center operations management.*

same data set from RUL and providing data set loopback to the outset of data collection and condition monitoring as plan-do-check-act (PDCA) continuous process. The PPM produces data set for CBM database at the first round and the next rounds will generate data training for fault diagnostics, prognostics and predictive maintenance. CBM can leverage as the strategic approach to guarantee the availability of the entire PDS of data center by monitoring from the device level down as transformers, generators, transfer switches, breakers and switches, UPSs, batteries, PDUs, and PSUs. CBM will manipulate as recursive function of data collection process.

The PDS of data center Tier IV had deliberated as maintenance model management (MMM) for constructing CBM of PDS, as illustrated in single line diagram of **Figure 9**. The critical devices and systems, which simulate to MMM all data derive from IEEE 493 Gold Book [17] and former research models of Wiboonrat [18, 19].

The devices and systems list, in **Table 2**, presents the quantifying characteristics of unit produced per year, number of failure, failures rate per year, MTBF, and MTTR. The following list of power devices in **Table 2** (active and supported distribution path) concentrates on the online monitoring data, which desire as input data for CBM and prognostic process for RUL [20].

The power reliability assessment of PDS needs to measure throughout the overall statuses of the PDS devices and systems of data center that comprise as the following [21]:

• Leaded acid batteries

**Figure 9.**

**45**

• Distribution switchgear

• Power distribution unit (PDU)

*Single line diagram of PDS of datacenter tier IV.*

*Condition-Based Maintenance for Data Center Operations Management*

*DOI: http://dx.doi.org/10.5772/intechopen.93945*

• Rack-Power supply unit (PSU)

The capacity analysis of power systems has investigated to diagnose and analyze of all power devices and systems as above list. The MMM designs to perform as PPM of PDS of data center Tier IV. All critical devices have been derived data set of


*Condition-Based Maintenance for Data Center Operations Management DOI: http://dx.doi.org/10.5772/intechopen.93945*

**Figure 9.** *Single line diagram of PDS of datacenter tier IV.*


The capacity analysis of power systems has investigated to diagnose and analyze of all power devices and systems as above list. The MMM designs to perform as PPM of PDS of data center Tier IV. All critical devices have been derived data set of

same data set from RUL and providing data set loopback to the outset of data collection and condition monitoring as plan-do-check-act (PDCA) continuous process. The PPM produces data set for CBM database at the first round and the next rounds will generate data training for fault diagnostics, prognostics and predictive maintenance. CBM can leverage as the strategic approach to guarantee the availability of the entire PDS of data center by monitoring from the device level down as transformers, generators, transfer switches, breakers and switches, UPSs, batteries, PDUs, and PSUs. CBM will manipulate as recursive function of data collection process. The PDS of data center Tier IV had deliberated as maintenance model management (MMM) for constructing CBM of PDS, as illustrated in single line diagram of **Figure 9**. The critical devices and systems, which simulate to MMM all data derive from IEEE 493 Gold Book [17] and former research models of Wiboonrat [18, 19]. The devices and systems list, in **Table 2**, presents the quantifying characteristics of unit produced per year, number of failure, failures rate per year, MTBF, and MTTR. The following list of power devices in **Table 2** (active and supported distribution path) concentrates on the online monitoring data, which desire as input data

The power reliability assessment of PDS needs to measure throughout the over-

all statuses of the PDS devices and systems of data center that comprise as the

for CBM and prognostic process for RUL [20].

*PPM system flow diagram of data center operations management.*

*Operations Management - Emerging Trend in the Digital Era*

following [21]:

**44**

**Figure 8.**

• Transformer

• Entrance switchgear

• Diesel generator

• Automatic transfer switch (ATS)

• Uninterruptable power supply (UPS)


**Table 2.**

*IEEE 493 active equipment MTBF.*

MTBF and MTTR from IEEE 493 [17] for each category as represented in **Table 1**. This method is defined the set-points of P-F curve according to the points where failure starts to occur and point where operators can find out that devices or systems are revealed the failing point (potential failure) because CBM is moving point P (potential failure) to the earliest time possible, the condition is to maximize the P-F interval [22].

#### **4. Preventive and predictive maintenance**

According to the data center operations and maintenance under PPM, online condition-monitoring systems are the best scenario by deploying DCIM software. The DCIM design of the PDS is option from reducing long-term operating costs and complexity. The efficient DCIM is being evolution to the automatic processes as the critical success factor for maintaining downtime. By self-diagnosis of DCIM, PDS devices and systems can track age, operating hours, working statuses, warning alarms, MTBF, MTTR, and the last modified or upgraded by who and when.

In this deliberation, researcher has installed StruxureWare [15], a DCIM software from Schneider Electric as sensing instrument for data collection. StruxureWare performs as points of online data collection by measuring all values at set points on the devices or systems, as shown in **Figure 10**. These data are online and real-time verifying with outset-determined data from CBM database to impose the critical levels as basic criteria. Control levels (before critical level) are ordinarily imposed for apprising automatic warnings before system shutdown. The types of automatic warning are depending on the severe consequence of the cascading failure. It has a process to send warning message to each personal mobile or e-mail by configuration. The foundation of StruxureWare is relied on transducers, sensors, networking and intelligent electronic devices (IED) for collecting data throughout the PDS in data center devices [23].

**4.1 CBM model for StruxureWare (DCIM)**

*Condition-Based Maintenance for Data Center Operations Management*

*DOI: http://dx.doi.org/10.5772/intechopen.93945*

*Data collection from PDS of data center tier IV.*

maintenance.

**47**

**Figure 10.**

Tracking the increasing probability of future failure of device or system is primary function of CBM. Extrapolating and predicting system condition over time will help to analyze particular devices that could possibly to have defects requiring repairs. A CBM method also diagnoses, through statistics and data, which devices or systems most likely will remain in acceptable condition without the requirement for *Condition-Based Maintenance for Data Center Operations Management DOI: http://dx.doi.org/10.5772/intechopen.93945*

**Figure 10.**
