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

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 maintenance.

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

E2-120 Battery, lead acid, strings 3215.30 24.00 0.01 1,173,590.30 32.13

**year**

**Failures Failure rate (failures/ year)**

840.20 0.00 0.00 14,432,242.40 0.00

732.50 3.0 0.00 2,139,024.00 37.33

690.30 22.00 0.03 274,853.50 1.64

266.00 115.00 0.58 15,033.80 25.74

426.40 4.00 0.01 933,708.00 2.00

322.70 0.00 0.00158 5,543,247.10 0.00

**MTBF (hrs.) MTTR**

**(hrs.)**

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 soft-

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

ware from Schneider Electric as sensing instrument for data collection.

the P-F interval [22].

*IEEE 493 active equipment MTBF.*

**Table 2.**

**4. Preventive and predictive maintenance**

**Category Class Unit/**

*Operations Management - Emerging Trend in the Digital Era*

>1500 kVA < =3000 kVA

>5 kV, all cabinets, ckt. bkrs. not included

>600A

packaged, 250 kW to 1.5 MW, continuous

floor

=600 V, all cabinets, ckt. bkrs. not included

E38-113 Transformer, dry, air cooled,

E36-230 Switchgear, insulated bus,

E34-110 Switch, automatic transfer,

E18-121 Diesel engine generator,

E39-200 UPS, small computer room

E36-210 Switchgear, insulated bus, <

the PDS in data center devices [23].

**46**

Since, Uptime Institute [11] and BICSI [12] have defined the data center Tier IV and Class F4 as the standard design for the data center site availability at 99.995 percent. The investigation of system reliability of PDS data center is an objective for this research model. Researcher has designed 12 sensor points by installed IED devices for data collection points throughout the PDS of data center [24]. The StruxureWare had installed and applied the concept of CBM to verify PDS of data center in only one single line diagram. Each devices and systems are differed functions in electrical and mechanical design proposes. Therefore, each device and system needs different location for installing and collecting data at the level of physical contact. All IED data collection must be measured in term of instantaneous and trending of all electrical status such as voltage, amperage, phase, total harmonic distortion (THD); and mechanical status; alarm, vibration, noise, temperature, leakage, oil level or other status; equipment aging, run-time, failure history, degradation percentage, abnormal events [25], as presented in **Figure 10**.

3.Replacement of parts, changing lubrication or changing spare parts could be executed during operations as supplier's recommendation for critical devices

(potential failure) to the earliest time possible maximizing the P-F interval

Field data collection is the beginning of CBM process. As the single line diagram of PDS of data center appointed 12 equipment installations for StruxureWare by set-point value as specify in **Table 1**, and status monitoring as specify in **Table 3**. The maintenance set-point value at the beginning refers from IEEE 493, MTBF, plus condition of P-F interval. Mostly, device status condition comes from supplier

without interrupting system operations (Concurrent Maintenance)

*Condition-Based Maintenance for Data Center Operations Management*

4.Extending aging for non-critical devices benefits when move point P

data sheet's for maintenance. Both of data collection sources are sending to StruxureWare, which intends for manipulating after; condition monitoring and data collection process; and data processing and signal processing. DCIM will execute function selection as operator's requirement and create statistic modeling for fault diagnostics and prognostics for calculating RUL. All data collection will input through the predictive maintenance function for setting up the new value and status as the beginning of condition monitoring, PDCA process, as represented in **Table 3**. Almost 12 months of data collection by StruxureWare and PPM model, there are no blackout in PDS of data center Tier IV. No blackout does imply no any device or system failure but Tier IV topology designs as fully redundancy 2(N + 1), therefor, some devices or systems can be failure but the other still perform without system interruption. The StruxureWare can detect and discover before sending information to administrator team to repair it under MTTR condition. Because data center Tier III is designed as 2 N and Tier IV is designed as 2(N + 1) topology. It allows more fault tolerance to devices and systems failure. The system warning occurs a few times but data center administrator can fix the problems by warning instruction from StruxureWare monitor guides. The StruxureWare has designed for easing to understand and predict any device or system failure and resolve it before it fails, which implies CBM help decrease planned and unplanned downtime, labor hours, and spare part inventory, while increases throughput of system productivity. Moreover, CBM supports the provision and early warning system for all devices and systems failure functions, StruxureWare has capable to controls inventory level much more effectively and no need as many emergency spare parts [26].

Idle server is a physical server that is still running but has no perform any computing resources or any transaction processing, that it consumes power but is serving no useful purpose. The Uptime Institute survey reports around 30 percent of global data center servers are either underutilization or completely idle. This server can consume power an impressive 175 watts when it is idle mode. A survey of server PSUs [27] reports the range of efficiency related to load of PSUs, as illus-

In the red zone, power loaded of PSU is lower than 20 percent the efficiency drops off precipitously. In the yellow zone, 20–40 percent, PSU efficiency begins to drop but typically exceeds 70 percent. In the green zone, the PSU operates above 40

before it has failed (functional failure).

*4.2.1 Value and status from condition monitoring systems*

**4.2 Value and status of data collections**

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

*4.2.2 Value and status from idle servers*

trated in **Figure 12**.

**49**

This CBM design proposes for extending P-F interval. StruxureWare shows data collecting from the last point at critical application server zone or Rack PSU, as depicted in **Figure 11**.

This helps data center administrator realizes the current power conditions when compares (Left PSU is 0.5 kW and Right PSU is 1.3 kW) to the maximum power capacity of each rack (4 kW) such as voltage, ampere, frequency, phase balance, temperature of the rack, space of rack available, and the last time audit. Moreover, this monitor from the device level up, from PSUs of each server to discover idle servers that are quietly draining power and taking up space.

The research presuppositions are:


**Figure 11.** *Data collection from rack PSU of data center tier IV by StruxureWare.*

